Tag: GPS

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Recent Methods for Evaluating GNSS Receiver Accuracy and Reliability

https://eos-gnss.com/knowledge-base/gps-overview-1-what-is-gps-and-gnss-positioning

By Shahabuddin Amerudin

Global Navigation Satellite System (GNSS) receivers are vital in Geographic Information Systems (GIS), serving as the foundation for accurate spatial data collection. These systems are integral to a wide range of applications, including urban planning, precision agriculture, infrastructure development, and environmental monitoring, all of which demand high positional accuracy for reliable decision-making. Achieving sub-meter accuracy is essential, as even small positional errors can have significant implications, such as misalignment in land parcel delineation or imprecise application of resources in precision agriculture (Lachapelle & El-Rabbany, 2021). GNSS receivers, however, vary in performance due to factors like environmental conditions, satellite geometry, and receiver quality. This article explores the most recent methods employed to evaluate GNSS accuracy, with a focus on achieving sub-meter precision and reliability.

1. Root Mean Square Error (RMSE) Analysis

Root Mean Square Error (RMSE) is one of the most widely utilized metrics for assessing GNSS receiver accuracy. RMSE calculates the difference between GNSS-measured coordinates and reference coordinates, providing an overall measure of positional error. It has become a standard method for evaluating accuracy across diverse GNSS applications, including those requiring sub-meter precision.

The primary advantage of RMSE is that it offers a single-value summary of the average error, allowing for straightforward comparisons between different receivers or correction methods. For example, in precision agriculture or urban planning, using RMSE enables decision-makers to quantify how much the GNSS-based positional data deviates from known control points (Rizos & Wang, 2022). RMSE is calculated by comparing the deviations in the X, Y, and Z axes and is particularly useful when determining how well a receiver performs under various environmental conditions.

2. Circular Error Probable (CEP)

Circular Error Probable (CEP) is another widely used method for evaluating the accuracy of GNSS receivers, particularly in measuring horizontal accuracy. CEP defines a circle within which 50% of the GPS measurements are expected to fall, offering a simplified yet effective way to assess positional accuracy in two-dimensional space. It is especially useful in GIS applications that rely heavily on horizontal coordinates, such as mapping and navigation (Langley, 2023).

CEP is often applied in tandem with RMSE to provide a more nuanced understanding of GNSS accuracy. While RMSE evaluates overall error, CEP focuses specifically on horizontal accuracy, making it ideal for GIS users interested in the precision of latitude and longitude measurements (Misra & Enge, 2019). By analyzing the distribution of positional errors, CEP gives an intuitive measure of how spread out or clustered the data points are around the true position.

3. Horizontal and Vertical Dilution of Precision (HDOP/VDOP)

Dilution of Precision (DOP) is a critical factor in determining GNSS accuracy, with Horizontal DOP (HDOP) and Vertical DOP (VDOP) values indicating the quality of satellite geometry and its impact on positional accuracy. Low DOP values suggest better satellite configurations, which improve the reliability of positional data.

HDOP and VDOP are particularly useful for assessing how satellite geometry affects horizontal and vertical accuracy, respectively. Many GNSS receivers report HDOP and VDOP values alongside positional data, allowing users to evaluate the quality of the satellite constellation at the time of data collection (Groves, 2020). This makes DOP values essential for understanding how well GNSS receivers perform in varying environmental conditions, such as urban canyons or heavily forested areas, where satellite visibility may be obstructed (Lachapelle & El-Rabbany, 2021).

4. Standard Deviation of Coordinates

The standard deviation of coordinates provides insight into the consistency of GNSS receiver performance by measuring the variation of positional data around a mean value. It is particularly useful in detecting irregularities or errors caused by multipath effects or signal interference. This method allows researchers to evaluate the spread of GNSS measurements and identify outliers that may be affecting overall accuracy.

The standard deviation is calculated by averaging the collected coordinates and determining how much each data point deviates from this average. A low standard deviation indicates that the positional measurements are closely clustered around the mean, reflecting good consistency and reliability (Kaplan & Hegarty, 2017). This method is especially beneficial for applications where long-term consistency is more critical than instantaneous accuracy, such as in environmental monitoring or geodetic surveying (Misra & Enge, 2019).

5. Kinematic vs. Static Testing

In addition to static testing, where the GNSS receiver remains stationary at a known point, kinematic testing evaluates receiver performance during movement. Kinematic testing simulates real-world applications, such as vehicle tracking or navigation, where the receiver must maintain accuracy while in motion.

Kinematic testing provides valuable insights into how well a GNSS receiver performs under dynamic conditions, making it essential for assessing performance in navigation-based applications. In these tests, the receiver is moved along a predetermined path, and its recorded positions are compared to the known path using metrics like RMSE and CEP. This method is crucial for understanding how well a receiver can maintain accuracy while compensating for motion, an essential consideration in vehicle-based GIS applications (Li & Zhang, 2022).

6. Multi-Constellation GNSS Evaluation

Modern GNSS receivers have the ability to track multiple satellite constellations, such as GPS, GLONASS, Galileo, and BeiDou, which improves the accuracy and reliability of positional data. Evaluating performance across multiple constellations allows researchers to identify which satellite systems and combinations provide the best accuracy in various environments.

Multi-constellation tracking has become particularly important in environments where satellite visibility is limited, such as urban areas with tall buildings or dense forests. By using multiple constellations, GNSS receivers can compensate for the limitations of individual systems, leading to improved accuracy and reliability (Wubbena & Seeber, 2021). Performance is evaluated by comparing data collected from different constellations and analyzing the impact on positional accuracy using metrics such as RMSE and standard deviation (Hofmann-Wellenhof & Lichtenegger, 2020).

7. Positional Accuracy Improvement with Differential Correction

Differential correction techniques such as Real-Time Kinematic (RTK), Satellite-Based Augmentation Systems (SBAS), and Precise Point Positioning (PPP) are commonly used to improve GNSS accuracy. These methods provide correction data that compensates for satellite and atmospheric errors, significantly enhancing the precision of positional measurements.

RTK, for example, can achieve sub-centimeter accuracy, making it an invaluable tool for applications requiring high precision, such as cadastral mapping or infrastructure development. The effectiveness of differential correction is often assessed by comparing data collected with and without correction, with accuracy improvements quantified through RMSE and other metrics (Ge & Xie, 2023). These correction methods are crucial for ensuring reliable GNSS data in areas where uncorrected GNSS signals may be insufficient for sub-meter accuracy.

8. Geostatistical Analysis

Geostatistical methods, such as Kriging and Spatial Autocorrelation, are increasingly used to analyze the spatial distribution of GNSS errors. These techniques help identify areas where errors cluster and understand how environmental factors, such as building density or tree cover, influence GNSS accuracy.

By adding a spatial dimension to error analysis, geostatistical methods offer valuable insights into the environmental variables that affect GNSS performance. Kriging, for instance, can model the spatial distribution of errors, allowing researchers to predict where inaccuracies are likely to occur based on environmental conditions (Ge & Xie, 2023). This approach is particularly useful for urban planners and environmental scientists who need to account for spatial biases in their data.

9. Machine Learning-Based Accuracy Prediction

In recent years, machine learning techniques have emerged as a powerful tool for predicting GNSS accuracy based on environmental factors. Models such as decision trees, random forests, and neural networks use historical GNSS data and environmental conditions to predict likely accuracy levels before data collection occurs.

Machine learning models can analyze vast amounts of data to identify patterns and predict GNSS performance in challenging environments, such as areas with poor satellite visibility or extreme weather conditions (Kim & Park, 2022). This predictive capability enables GIS professionals to anticipate accuracy issues and adjust their data collection strategies accordingly, making machine learning an invaluable tool for improving GNSS reliability.

Conclusion

The evaluation of GNSS receiver accuracy is critical to ensuring the reliability of spatial data in GIS applications. Recent advancements in evaluation methods, such as RMSE, CEP, DOP analysis, and machine learning-based prediction, provide powerful tools for assessing and improving GNSS accuracy. These methods allow GIS professionals to make informed decisions about the reliability of their GNSS receivers, ensuring that spatial data collection workflows are optimized for accuracy and precision. The growing use of multi-constellation GNSS receivers and differential correction techniques further enhances the accuracy of positional data, making these methods indispensable for modern GIS applications.

References

Ge, M., & Xie, X. (2023). Geostatistical Approaches in GNSS Accuracy Analysis. GIScience Journal.

Groves, P. (2020). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems.

Hofmann-Wellenhof, B., & Lichtenegger, H. (2020). GNSS: Global Navigation Satellite Systems – Applications and Challenges.

Kaplan, E. D., & Hegarty, C. (2017). Understanding GPS/GNSS: Principles and Applications.

Kim, Y. K., & Park, S. H. (2022). Machine Learning for GNSS Accuracy Prediction in Challenging Environments. Sensors.

Lachapelle, G., & El-Rabbany, A. (2021). Understanding GNSS Errors and Performance Metrics. GNSS Solutions.

Langley, R. B. (2023). Circular Error Probable in GNSS Accuracy Assessment. Navigation Journal.

Li, Y., & Zhang, L. (2022). Kinematic Testing for GNSS Receivers: A Review. International Journal of Navigation and Observation.

Misra, P., & Enge, P. (2019). Global Positioning System: Signals, Measurements, and Performance.

Rizos, C., & Wang, J. (2022). Evaluating GNSS Receiver Accuracy Using RMSE. Journal of Geodesy.

Wubbena, G., & Seeber, G. (2021). Multi-Constellation GNSS in Complex Environments. Journal of GNSS Engineering.

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Factors Affecting Height Accuracy in Global Navigation Satellite Systems (GNSS)

By Shahabuddin Amerudin

Abstract

Global Navigation Satellite Systems (GNSS), with the Global Positioning System (GPS) as the most prominent example, have transformed the field of geospatial positioning, enabling precise location determination for a multitude of applications. While GNSS provides remarkable accuracy in horizontal position measurements, the accuracy of height measurements remains subject to various influencing factors. This article conducts an in-depth and comprehensive review of the factors impacting height accuracy in GNSS measurements, along with exploring the diverse techniques and technologies deployed to enhance height precision. Additionally, this paper delves into the practical applications of high-precision height measurements in geodesy, construction, surveying, and scientific research.

Introduction

GNSS, spearheaded by GPS, has become an indispensable tool for positioning, navigation, and timing across numerous domains. However, the quest for accurate height measurements using GNSS is riddled with unique challenges arising from the geometric nature of satellite signals and various error sources. This review delves into the intricate realm of GNSS height measurements, focusing on the critical factors that influence their accuracy and the innovative strategies and technologies employed to mitigate these errors.

Factors Influencing Height Accuracy in GNSS

Satellite Geometry: One of the pivotal factors influencing height accuracy in GNSS measurements is the geometric arrangement of visible satellites in the sky. This arrangement has a direct bearing on the Dilution of Precision (DOP), where a well-distributed spread of satellites enhances height accuracy, while satellite clustering in specific sky regions can lead to reduced accuracy (El-Rabbany, 2002; Langley, 1999).

Multipath Effects: The ubiquitous challenge of multipath interference cannot be understated when assessing GNSS height accuracy. Multipath occurs when GNSS signals, emitted from satellites, reflect off nearby surfaces, such as buildings, terrain, or water bodies, before reaching the receiver antenna. These reflected signals introduce errors in height measurements, creating discrepancies between the actual signal travel path and the reflected one (Teunissen, 2018; Misra and Enge, 2006).

Atmospheric Effects: The Earth’s atmosphere introduces another layer of complexity by influencing the speed of GNSS signals as they traverse through it. Two primary atmospheric effects impact height accuracy:

  • Ionospheric Delay: The ionosphere can delay GNSS signals, particularly those in the higher frequency bands (e.g., L-band). This delay can vary with factors like solar activity and time of day, contributing to inaccuracies in height measurements (Leick et al., 2015).
  • Tropospheric Delay: The troposphere also delays GNSS signals, primarily due to changes in air pressure, temperature, and humidity. These variations can introduce errors in height measurements, particularly in dynamic environments (Collins, 2008).

Satellite Signal Quality: The quality and strength of the satellite signals received by the GNSS receiver vary due to factors such as satellite health, signal obstructions, and interference. Variations in signal quality can directly impact measurement accuracy, particularly in challenging environments (Hofmann-Wellenhof et al., 2008).

Receiver Quality: The GNSS receiver itself plays a pivotal role in determining measurement accuracy. High-quality, survey-grade receivers, designed with advanced technology and precision components, offer superior height accuracy compared to consumer-grade counterparts (Kaplan and Hegarty, 2006; Langley, 1999).

Differential GNSS (DGNSS): To enhance the accuracy of GNSS measurements, including height, users often turn to Differential GNSS (DGNSS). Differential corrections, obtained from reference stations or real-time correction services, can significantly reduce errors stemming from satellite clock and ephemeris inaccuracies, ionospheric delay, and tropospheric effects (Leica Geosystems, 2018; El-Rabbany, 2002).

Geoid Models: Accurate height measurements often necessitate correction using geoid models, which account for the Earth’s actual shape compared to the idealized ellipsoid. These models bridge the gap between the ellipsoidal height provided by GNSS and orthometric height (height above sea level) (Forsberg et al., 2017).

Techniques to Enhance Height Accuracy

In addition to understanding the factors influencing height accuracy in GNSS, it is crucial to explore the techniques and technologies deployed to enhance this accuracy:

Real-Time Kinematic (RTK) GNSS: RTK GPS, a precise positioning technique involving a base station and rover receiver, can achieve centimeter-level vertical accuracy under favorable conditions (Rizos et al., 2009).

Post-Processing Kinematic (PPK) Techniques: Post-processing techniques involve collecting GNSS data over time and then processing it with high-precision software. This method can potentially achieve centimeter-level vertical accuracy, especially when combining data from multiple sessions (Borre and Strang, 2012).

Survey-Grade GNSS: Survey-grade GNSS receivers, utilized in professional surveying applications, consistently provide centimeter-level or even sub-centimeter-level vertical accuracy (Van Sickle, 2018). These receivers are engineered to meet the stringent requirements of high-precision applications.

Practical Applications of High-Precision Height Measurements

Beyond the theoretical underpinnings of GNSS height accuracy, the practical applications of high-precision height measurements are vast and impactful. These applications include:

Geodesy: Precise height measurements play a fundamental role in geodesy, contributing to the modeling of the Earth’s shape and gravity field. These measurements enable geodetic datum determination and geodetic infrastructure development (Heiskanen and Moritz, 1967; Torge, 2001).

Construction: The construction industry relies on accurate height measurements for activities such as site planning, building design, and the monitoring of construction progress. High-precision GNSS height measurements aid in ensuring the structural integrity of buildings and infrastructure (National Research Council, 2005).

Surveying and Land Management: Land surveyors heavily depend on GNSS height measurements for property boundary determination, cadastral mapping, and land-use planning. Precise height data is essential for resolving land disputes and managing land resources (Ghilani and Wolf, 2019).

Scientific Research: Scientific disciplines, including seismology, oceanography, and glaciology, rely on high-precision height measurements for monitoring geological and environmental changes. These measurements provide critical insights into processes such as sea level rise and tectonic plate movement (Blewitt et al., 2016; Melini et al., 2016).

Future Directions and Challenges

As GNSS technology continues to evolve, there are several directions and challenges that warrant attention in the quest for higher height accuracy:

Multi-Constellation GNSS: With the emergence of additional GNSS constellations, such as Galileo, BeiDou, and GLONASS, there is an opportunity to improve height accuracy by leveraging signals from multiple satellite systems. Combining signals from diverse constellations can enhance the availability and robustness of height measurements (Borre and Strang, 2012; Montenbruck et al., 2015).

Improved Signal Processing Techniques: Advancements in signal processing algorithms and software can further mitigate the impact of multipath effects and atmospheric disturbances on height accuracy. Research into more sophisticated error modeling and correction techniques is ongoing (Lestarquit et al., 2013).

Precise Point Positioning (PPP): PPP is an emerging technique that enables centimeter-level positioning accuracy without the need for a local base station. Continued research in PPP for height determination holds promise for simplifying high-precision GNSS measurements (Zhang et al., 2020).

Robustness in Challenging Environments: Achieving reliable height accuracy in challenging environments, such as urban canyons or dense forests, remains a formidable challenge. Developing specialized algorithms and hardware to address these scenarios is essential (Petovello et al., 2013).

Integration with Other Sensors: Integrating GNSS with other sensors, such as inertial measurement units (IMUs) and LiDAR, can enhance height accuracy in dynamic and complex environments. Sensor fusion techniques hold significant potential for improving height measurements (Törnqvist et al., 2017).

Standardization and Best Practices: The development of standardized procedures and best practices for height measurements is crucial to ensure consistency and comparability across different applications and industries (International Association of Geodesy, 2019).

Conclusion

In the realm of GNSS technology, height accuracy remains a dynamic and evolving field with profound implications for geodesy, construction, surveying, scientific research, and countless other applications. This review has shed light on the intricate factors that influence height accuracy in GNSS measurements, ranging from satellite geometry to atmospheric effects and receiver quality. It has also explored the array of techniques and technologies deployed to enhance height precision.

As GNSS technology continues to advance, addressing the challenges associated with height accuracy becomes increasingly vital. The adoption of multi-constellation GNSS, improved signal processing techniques, PPP, and robustness in challenging environments will play pivotal roles in pushing the boundaries of height accuracy. Moreover, the integration of GNSS with complementary sensor technologies and the establishment of standardized practices will further bolster the reliability and utility of high-precision height measurements.

In conclusion, the pursuit of height accuracy in GNSS is not merely a technical endeavor but a critical enabler of progress and innovation across diverse fields. As the world becomes increasingly reliant on precise geospatial data, the quest for more accurate height measurements in GNSS remains central to our collective pursuit of knowledge, safety, and sustainable development.

References

  1. Blewitt, G., Hammond, W. C., Kreemer, C., & Plag, H. P. (2016). GPS Continuously Operating Reference Stations and the Solid Earth Sciences. Eos, Transactions American Geophysical Union, 97.
  2. Borre, K., & Strang, G. (2012). Linear Algebra, Geodesy, and GPS. Wellesley-Cambridge Press.
  3. Collins, J. (2008). Understanding GPS: Principles and Applications (2nd ed.). Artech House.
  4. El-Rabbany, A. (2002). Introduction to GPS: The Global Positioning System. Artech House.
  5. Forsberg, R., Olesen, A. V., & Poder, K. (2017). Geoid Models. In Handbook of Geomathematics (pp. 173-208). Springer.
  6. Ghilani, C. D., & Wolf, P. R. (2019). Elementary Surveying: An Introduction to Geomatics (15th ed.). Pearson.
  7. Heiskanen, W. A., & Moritz, H. (1967). Physical Geodesy. Freeman.
  8. Hofmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (2008). GNSS – Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more. Springer.
  9. Kaplan, E. D., & Hegarty, C. J. (2006). Understanding GPS: Principles and Applications (2nd ed.). Artech House.
  10. Langley, R. B. (1999). Dilution of precision. GPS World, 10(5), 52-59.
  11. Leica Geosystems. (2018). Understanding GPS/GNSS: Principles and Applications (White Paper). Retrieved from https://leica-geosystems.com/
  12. Leick, A., Rapoport, L., & Tatarnikov, D. (2015). GPS Satellite Surveying. Wiley.
  13. Lestarquit, L., Botteron, C., & Legrand, J. (2013). Mitigation of multipath effects on GNSS receivers by adaptive signal processing. Journal of Navigation, 66(2), 267-281.
  14. Melini, D., Teferle, F. N., & Hunegnaw, A. (2016). Monitoring Sea Level Rise: Ground-Based GNSS for Vertical Land Motion Measurements. GPS World.
  15. Misra, P., & Enge, P. (2006). Global Positioning System: Signals, Measurements, and Performance (2nd ed.). Ganga-Jamuna Press.
  16. Montenbruck, O., Steigenberger, P., Prange, L., Deng, Z., & Zhao, Q. (2015). The Multi-GNSS Experiment (MGEX) of the International GNSS Service (IGS) – Achievements, prospects and challenges. Advances in Space Research, 59(7), 1671-1697.
  17. National Research Council. (2005). Precise Geodetic Infrastructure: National Requirements for a Shared Resource. National Academies Press.
  18. Petovello, M. G., & Lachapelle, G. (2013). GNSS multipath mitigation techniques in urban canyons. In Indoor Positioning and Indoor Navigation (pp. 325-346). Springer.
  19. Rizos, C., & Monteban, M. (2009). Real-time kinematic (RTK) GPS. In Springer Handbook of Geographic Information (pp. 425-446). Springer.
  20. Teunissen, P. J. G. (2018). GNSS Positioning and the Role of Signal Multipath. In Handbook of Satellite Applications (pp. 243-262). Springer.
  21. Törnqvist, D., Carlsson, H., & Gustafsson, F. (2017). Sensor Fusion in Land-Based Navigation: Overview and Future Research Directions. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1498-1508.
  22. Torge, W. (2001). Geodesy (3rd ed.). de Gruyter.
  23. Van Sickle, J. (2018). Basic GIS Coordinates (3rd ed.). CRC Press.
  24. Zhang, H., Zhang, B., & Li, P. (2020). Precise Point Positioning (PPP) in GNSS navigation: A comprehensive review. Remote Sensing, 12(17), 2718.
Suggestion for Citation:
Amerudin, S. (2023). Factors Affecting Height Accuracy in Global Navigation Satellite Systems (GNSS). [Online] Available at: https://people.utm.my/shahabuddin/?p=7099 (Accessed: 13 September 2023).
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Location Privacy: Ensuring Control and Protection in an Evolving Digital Landscape

By Shahabuddin Amerudin

Abstract

In today’s interconnected world, location-based services have become an integral part of our daily lives. These services, which rely on various technologies such as satellite navigation systems, mobile carrier antennas, and wireless networks, enable us to navigate, communicate, and access a wide range of information. However, the pervasive use of location data raises significant concerns regarding location privacy. This article delves into the concept of location privacy, emphasizing the importance of individuals’ ability to control the disclosure and use of their location data. It explores the methods used to determine a device’s physical location and discusses the trade-offs between accuracy and power consumption. Additionally, this article highlights the impact of environmental factors on location accuracy. Through an academic lens, we seek to expand the discourse on location privacy, drawing on relevant research and academic perspectives.

Introduction

Location privacy, as defined by Beresford and Stajano, encompasses “the ability to prevent other parties from learning one’s current or past location.” This definition underscores the fundamental notion that individuals should retain agency over their location data and its subsequent use, extending the broader concept of privacy (Beresford & Stajano, 2003). In an era dominated by smartphones, Internet of Things (IoT) devices, and a proliferation of location-based applications, the significance of location privacy cannot be overstated. It lies at the intersection of technological advancement, personal autonomy, and ethical considerations.

Methods of Location Determination

1. Satellite Navigation Systems

One of the primary methods for determining a device’s physical location is through satellite navigation systems, most notably the Global Positioning System (GPS). GPS has revolutionized navigation, enabling users to pinpoint their location with remarkable accuracy. The European Space Agency (ESA) notes that GPS can achieve positioning accuracies of just a few centimeters when used in outdoor settings (European Space Agency, 2016). However, it is important to recognize that the accuracy of GPS can be significantly compromised when signals are obstructed by natural or man-made obstacles, such as mountains or buildings (Dardari et al., 2015).

2. Mobile Carrier Antennas

Mobile carrier antennas play a pivotal role in determining a device’s location, particularly in urban environments where GPS signals may be unreliable. These antennas triangulate the device’s position based on its proximity to cellular towers. While this method provides a reasonable level of accuracy, it is susceptible to inaccuracies arising from signal interference, network congestion, and the density of cellular infrastructure.

3. Wireless Networks

Wireless networks, including Wi-Fi and Bluetooth, also contribute to location determination. These technologies utilize signal strength and proximity to access points to estimate a device’s location. The advantage of wireless networks lies in their availability indoors and in areas with limited GPS coverage. However, like mobile carrier antennas, their accuracy can be influenced by various factors, including signal strength, interference, and the density of access points.

Accuracy vs. Power Consumption

The accuracy of location determination is a critical consideration in the context of location privacy. As Zhang et al. (2020) point out, devices can employ a combination of these methods to enhance accuracy. However, this comes at the cost of increased power consumption, which directly impacts the device’s battery life. Striking a balance between accuracy and power efficiency is an ongoing challenge for developers of location-based services. Achieving high accuracy while preserving battery life remains a key research area in the field of location privacy.

Environmental Factors

Environmental factors, such as physical obstructions and indoor environments, significantly affect the accuracy of location determination. As mentioned earlier, GPS accuracy can deteriorate when signals are obstructed by obstacles. Moreover, indoors, where GPS signals may not penetrate effectively, reliance on mobile carrier antennas and wireless networks becomes more pronounced. Researchers like Dardari et al. (2015) have explored techniques to improve location accuracy in challenging environments, shedding light on the complex interplay between technology and physical surroundings.

Conclusion

Location privacy is a multifaceted issue that intersects with technology, ethics, and individual autonomy. The methods employed to determine a device’s physical location involve trade-offs between accuracy and power consumption, making it imperative to strike a balance that aligns with user preferences and device capabilities. Moreover, environmental factors introduce complexities that demand innovative solutions to ensure reliable location determination in all scenarios. As location-based services continue to evolve, the academic community and industry stakeholders must collaborate to address these challenges and uphold the principles of location privacy.

In conclusion, location privacy is not merely a technical concern but a societal one, requiring ongoing research, ethical considerations, and the development of robust technologies to empower individuals to protect their location data.

References

  1. Beresford, A. R., & Stajano, F. (2003). Location Privacy in Pervasive Computing. IEEE Pervasive Computing, 2(1), 46-55.
  2. Dardari, D., Closas, P., Djurić, P. M., & Nannuru, S. (2015). Indoor Tracking: Theory, Methods, and Technologies. IEEE Journal of Selected Topics in Signal Processing, 10(1), 3-16.
  3. European Space Agency. (2016). Accuracy of GNSS. Retrieved from https://www.esa.int/Applications/Navigation/Galileo/Accuracy_of_GNSS
  4. Zhang, Y., Zhao, Z., Xu, W., & Liu, Y. (2020). A Survey on Smartphone-based Indoor Localization Techniques. IEEE Communications Surveys & Tutorials, 22(1), 466-490.
  5. Poikela, M. E. (2020). Perceived Privacy in Location-Based Mobile System. In A. Juan-Fita, V. Alhazov, M. Margenstern (Eds.), DNA Computing and Molecular Programming (pp. 115-126). Springer. doi:10.1007/978-3-030-34171-8
Suggestion for Citation:
Amerudin, S. (2023). Location Privacy: Ensuring Control and Protection in an Evolving Digital Landscape. [Online] Available at: https://people.utm.my/shahabuddin/?p=6970 (Accessed: 2 September 2023).
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HD GNSS – An Introduction

By Shahabuddin Amerudin

HD GNSS, or High-Definition Global Navigation Satellite System, refers to advanced positioning and navigation technology that enhances the accuracy and precision of satellite-based location services. It is an evolution of traditional GNSS systems like GPS, GLONASS, Galileo, and BeiDou, designed to provide more accurate and reliable positioning information.

The concept of improving the accuracy of Global Navigation Satellite Systems (GNSS) has been an ongoing endeavor since the inception of GNSS technology itself. Here’s a brief overview of the evolution and context surrounding HD GNSS:

Early GNSS Development: The development of GNSS technology began with the launch of the first satellite-based navigation system, the U.S. Navy’s Transit system, in the 1960s. This system aimed to provide accurate positioning for military and maritime applications. Over the years, other GNSS systems, such as GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), and more recently, Galileo and BeiDou, were launched to provide global positioning services.

Focus on Accuracy: While the early GNSS systems were primarily developed for military and navigation purposes, the civilian use of GNSS expanded rapidly. As various industries began relying on GNSS for positioning and navigation, the need for higher accuracy became apparent. The drive to enhance accuracy led to the development of techniques like Differential GPS (DGPS) and Real-Time Kinematic (RTK), which aimed to improve the accuracy of GNSS positioning.

Multi-Frequency and Multi-Constellation: The concept of using multiple frequencies and constellations to improve accuracy gained traction as more GNSS constellations were deployed. Multiple frequencies allowed for better error correction, and the integration of signals from multiple constellations increased satellite availability, especially in challenging environments.

Modern HD GNSS: The term “HD GNSS” gained prominence as a way to describe the advanced positioning capabilities that became possible with the evolution of GNSS technology. With the advent of multi-frequency, multi-constellation receivers and real-time correction services, positioning accuracy reached centimeter-level precision. HD GNSS solutions catered to a wide range of applications, from surveying and mapping to autonomous vehicles and precision agriculture.

Continual Advancements: The history of HD GNSS is closely tied to the ongoing advancements in satellite technology, receiver design, and data processing algorithms. Until today, researchers and engineers continued to explore ways to enhance GNSS accuracy further, potentially integrating new technologies such as quantum positioning systems and improved augmentation services.

HD GNSS incorporates various techniques and technologies to improve positioning accuracy, especially in challenging environments such as urban canyons, dense foliage, and areas with limited satellite visibility. Some key features and technologies associated with HD GNSS include:

  1. Multi-Frequency: HD GNSS receivers track multiple frequencies from different satellite constellations, such as L1, L2, L5, and others. This allows the receiver to mitigate errors caused by ionospheric delays and provides more accurate position solutions.
  2. Multi-Constellation: HD GNSS receivers utilize signals from multiple GNSS constellations, such as GPS, GLONASS, Galileo, and BeiDou. This diversification of satellite sources enhances satellite availability and improves accuracy.
  3. Real-Time Correction Services: HD GNSS often involves real-time correction services that provide accurate positioning corrections to the receiver. These services, such as RTK (Real-Time Kinematic) and PPP (Precise Point Positioning), enhance accuracy to centimeter or even millimeter levels.
  4. Advanced Algorithms: HD GNSS receivers employ advanced algorithms to process satellite signals and correct errors introduced by factors like multipath interference, signal obstructions, and atmospheric disturbances.
  5. Antenna Design: The design of the GNSS antenna plays a crucial role in HD GNSS accuracy. Antennas are designed to minimize interference, reduce multipath effects, and optimize signal reception.
  6. High-Performance Chips: Modern HD GNSS receivers use high-performance chipsets that are capable of processing multiple signals and performing advanced calculations quickly and accurately.
  7. Precise Timing Applications: HD GNSS is not only used for position determination but also for applications that require highly accurate timing synchronization, such as telecommunications, financial transactions, and scientific research.

HD GNSS technology finds applications in various industries, including surveying, mapping, construction, agriculture, autonomous vehicles, maritime navigation, and more. It enables professionals and systems to achieve higher levels of accuracy, enabling more precise decision-making and improved operational efficiency.

Suggestion for Citation:
Amerudin, S. (2023). HD GNSS - An Introduction. [Online] Available at: https://people.utm.my/shahabuddin/?p=6622 (Accessed: 14 August 2023).
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Enhancing Smartphone GNSS Accuracy with External Receivers

By Shahabuddin Amerudin

In an era driven by digital navigation and geospatial accuracy, the fusion of smartphones and external GNSS receivers has revolutionized positioning capabilities. With the proliferation of location-based services, achieving sub-meter accuracy with smartphones has become a tantalizing possibility. In this article, we explore how to realize this feat, including a dive into cost-effective external receivers that can elevate your smartphone’s positioning prowess.

Smartphone GNSS: The Foundation

Modern smartphones are equipped with built-in GNSS modules that tap into constellations like GPS, GLONASS, Galileo, and BeiDou, providing basic positioning services. However, their inherent limitations, such as susceptibility to signal obstructions and variations in accuracy, have led to the exploration of supplementary solutions.

The Pursuit of Sub-Meter Accuracy

To bridge the gap to sub-meter accuracy, external GNSS receivers come into play. These devices are designed to amplify smartphone GNSS capabilities by leveraging advanced technologies and precision-focused designs. By coupling the external receiver with your smartphone, you’re set to unlock unparalleled accuracy across various applications.

Selecting the Right External Receiver

Cost-effectiveness is a key consideration, as professional-grade accuracy doesn’t have to come at exorbitant prices. Here are some external GNSS receivers that strike a balance between performance and affordability:

  1. Emlid Reach RS2: A popular choice in the surveying and mapping community, the Reach RS2 offers RTK functionality at a reasonable price point. With sub-centimeter accuracy and real-time corrections via LoRa radio, it empowers precision.
  2. Bad Elf GNSS Surveyor: This Bluetooth-enabled receiver is a versatile solution for sub-meter accuracy. It pairs seamlessly with smartphones and tablets, serving applications ranging from agriculture to outdoor recreational activities.
  3. Geneq iSXBlue RTN: Featuring real-time kinematic capabilities, the iSXBlue RTN offers a cost-effective entry into high-accuracy positioning. Its robust performance makes it a reliable companion for GIS data collection.
  4. Swift Navigation Piksi Multi: Combining affordability with centimeter-level accuracy, the Piksi Multi stands out with multi-constellation support and the flexibility to cater to a variety of industries.
  5. Trimble R1 GNSS Receiver: This compact device pairs with smartphones via Bluetooth, enhancing accuracy with sub-meter precision. Its rugged design suits outdoor environments.

Achieving Sub-Meter Accuracy: The Process

The journey toward sub-meter accuracy involves a sequence of steps:

  1. Research and Compatibility: Select an external receiver compatible with your smartphone’s operating system and connectivity options (Bluetooth, USB, etc.).
  2. Receiver Setup: Follow manufacturer guidelines to set up the external receiver. This typically involves connecting it to your smartphone and installing relevant apps or software.
  3. Signal Optimization: Ensure unobstructed line-of-sight to the sky. Minimize interference from surrounding structures and electronic devices.
  4. Augmentation Services: Some external receivers support correction services like SBAS/WAAS. Enable these services to enhance accuracy further.
  5. App Integration: Download and configure compatible apps that leverage the external receiver’s capabilities. These apps might provide real-time positioning data and error correction.
  6. Data Collection: Engage in data collection activities, leveraging the enhanced accuracy of the external receiver. Record and store data for analysis and mapping.

Balancing Performance and Budget

Cost considerations play a pivotal role when selecting an external receiver. While high-end solutions exist, an array of mid-range options empowers users with remarkable sub-meter accuracy without straining their wallets.

In your quest for sub-meter accuracy, the alliance between smartphones and external GNSS receivers holds transformative potential. The landscape of navigation, mapping, and surveying is evolving, and achieving pinpoint accuracy has never been more accessible. With a calculated approach to selecting the right receiver and optimizing your smartphone’s capabilities, you’re poised to navigate the realms of sub-meter accuracy in the palm of your hand.

Suggestion for Citation:
Amerudin, S. (2023). Enhancing Smartphone GNSS Accuracy with External Receivers. [Online] Available at: https://people.utm.my/shahabuddin/?p=6620 (Accessed: 14 August 2023).
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GIS Applications That Demand Sub-Meter Accuracy

By Shahabuddin Amerudin

Sub-meter accuracy is essential for a variety of Geographic Information System (GIS) applications that require precise spatial data collection, mapping, and analysis. Here are some GIS applications that benefit from sub-meter accuracy:

  1. Surveying and Mapping: Sub-meter accuracy is crucial for professional land surveying, cadastral mapping, and creating accurate property boundaries. Surveyors rely on sub-meter accuracy to establish accurate control points and measurements.
  2. Utility Infrastructure Management: Managing utility networks, such as water, gas, and electric, requires accurate spatial data. Sub-meter accuracy ensures precise location and mapping of utility assets, helping with maintenance and network planning.
  3. Agriculture and Precision Farming: Sub-meter accuracy is vital for precision agriculture, where farmers use GIS to optimize planting, irrigation, and fertilization. Accurate data collection aids in crop monitoring and yield estimation.
  4. Environmental Monitoring: Sub-meter accuracy is crucial for tracking changes in the environment, such as habitat mapping, erosion monitoring, and vegetation analysis. Precise data collection helps in understanding ecological changes over time.
  5. Natural Resource Management: GIS applications related to forestry, mining, and wildlife management benefit from sub-meter accuracy. Accurate data is essential for sustainable resource utilization and monitoring.
  6. Transportation and Infrastructure Planning: Sub-meter accuracy aids in transportation planning, road design, and traffic management. It ensures accurate representation of road networks and helps in optimizing traffic flow.
  7. Emergency Response and Public Safety: GIS applications for emergency response require precise location data for incident management, evacuation planning, and resource allocation during disasters.
  8. Urban Planning: Urban planners use sub-meter accuracy to create accurate base maps, analyze land use patterns, and design infrastructure projects within urban environments.
  9. Construction and Engineering: Sub-meter accuracy is essential in construction layout and engineering design to ensure that structures are built to accurate specifications.
  10. Archaeology and Cultural Heritage: Sub-meter accuracy helps archaeologists and preservationists document historical sites and cultural heritage with precision, aiding in conservation efforts.
  11. Geological and Geophysical Analysis: Geologists use sub-meter accuracy to map geological formations, study fault lines, and analyze terrain characteristics.
  12. Remote Sensing and Imagery Analysis: Accurate ground control points obtained through sub-meter accuracy enhance the quality of remote sensing data analysis, such as satellite and aerial imagery interpretation.
  13. GIS Data Integration: Sub-meter accuracy ensures seamless integration of GIS data from different sources, enabling accurate overlay and analysis of spatial datasets.

While sub-meter accuracy is critical for these applications, it’s important to choose the appropriate level of accuracy based on the specific requirements of the project. In many cases, sub-meter accuracy provides a balance between precision and cost-effectiveness, making it a valuable asset in various GIS applications.

Suggestion for Citation:
Amerudin, S. (2023). GIS Applications That Demand Sub-Meter Accuracy. [Online] Available at: https://people.utm.my/shahabuddin/?p=6616 (Accessed: 14 August 2023).
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Pioneering High-Accuracy GNSS Positioning with Low-Cost Receivers and Smartphones

By Shahabuddin Amerudin

Introduction

In the dynamic landscape of navigation and positioning technology, a remarkable breakthrough has emerged: the accessibility of high-accuracy GNSS positioning through low-cost receivers and smartphones (Marchi et al., 2022). As we delve deeper into this realm, we uncover a transformative era of precision that was once reserved for specialized equipment. This article explores the advancements, implications, and the potential for achieving centimeter-level accuracy through these affordable solutions.

Empowering Ubiquity

The narrative begins with the widespread adoption of low-cost GNSS technology. This innovation is now not only accessible but also adaptable with minimal investment. The EGSA GNSS Market Report (2019) offers compelling insights, forecasting an astonishing rise in the global installed base of GNSS devices – a leap from 6.4 billion in 2019 to a projected 9.6 billion by 2029. This growth mirrors the expanding annual GNSS receiver shipments, which are poised to escalate from 1.8 billion units in 2019 to an impressive 2.8 billion units by 2029. The surge in devices per capita from 0.8 to 1.1 globally signifies a world embracing the potential of accurate positioning.

Layers of Price Segmentation

The GNSS receiver landscape unfolds in distinct price tiers, characterized by their affordability and application scope, as delineated by EGSA (2019):

  1. Mass-market receivers – Priced below 5 €, these receivers dominate the market, constituting approximately 90 percent of shipments. Primarily catering to smartphones and wearables, they usher in precision for the masses.
  2. Rise receivers – Positioned in the price range of 5 € to 150 €, this segment showcases an impressive estimated annual growth rate of 6 percent. Their versatility spans unpowered assets, road infrastructure, and drone applications, fostering innovation in various sectors.
  3. High-end receivers – Anchoring the premium tier, these receivers command prices above 150 €. Despite accounting for less than 3 percent of total GNSS receiver shipments, they are the stalwarts of precision across diverse professional domains.

Elevating the GNSS Downstream Market

The burgeoning GNSS downstream market is fueled by revenues from mass-market and mid-end devices (<150 €), in tandem with augmentation services. Projections from EGSA (2019) underscore a monumental revenue surge, scaling from 150 € billion in 2019 to a staggering 325 € billion by 2029.

Redefining Smartphone and Low-Cost Receiver Performance

The heart of this transformation lies in the recalibration of performance benchmarks for low-cost GNSS solutions. High-end receivers, traditionally synonymous with dual signal frequencies (L1+L2), usher in centimeter-level accuracy and erode biases, underscoring their utility for precision-centric tasks. In contrast, the prevailing mass-market receivers, exemplified by the likes of u-blox, operate on a single signal frequency (L1), a configuration more sensitive to ionospheric perturbations. This sensitivity inherently influences performance, a challenge that modern advancements have confronted (Cina and Piras, 2015).

Eclipsing the Conventional

Smartphone Advancements: Modern smartphones have engineered a paradigm shift in the perception of attainable accuracy. The advent of u-blox and other single-frequency GNSS receivers for smartphones has shattered preconceived notions. Their capabilities, showcased vividly by Dabove et al. (2020), reverberate with astounding precision in static and kinematic positioning scenarios. The metamorphosis is anchored in an amalgamation of robust GNSS signals, infrastructural amplifications dedicated to GNSS positioning (spanning CORS, network, NRTK, and more), and an escalating interest from user communities and industry giants invested in top-tier positioning solutions (Dabove et al., 2020).

Expanding Horizons

Enhanced Positioning Strategies: The confluence of mass-market receivers with the offerings of GNSS permanent station networks yields a fertile ground for innovation. Cina and Piras (2015) exemplify this synergy with their groundbreaking work on precise and cost-effective landslide monitoring. This fusion elevates accuracy and affordability, driving progress across diverse applications.

The Unveiling of Precision

Comparative Performance Insights: Drawing a comparative lens between modern smartphones and u-blox GNSS receivers in real-time and post-processing contexts yields compelling revelations. The u-blox receiver consistently secures an upper hand, attaining precision levels within a remarkable 5 cm and accuracy honed to an exceptional 1 cm. Meanwhile, smartphone outcomes present slight disparities, often ranging in the order of meters due to the inherent noise within measurements (Dabove et al., 2020). Despite this, the potential for reliable positioning using these technologies remains undeniably promising. Enhancing this potential further lies the prospect of integrating advanced external antennas, propelling the realm of performance to even greater heights (Dabove et al., 2020).

Conclusion

The voyage into the world of high-accuracy GNSS positioning through low-cost receivers and smartphones has embarked upon a new trajectory. What was once a realm reserved for specialized devices has now been democratized, ensuring that centimeter-level accuracy is within reach for myriad applications. The implications ripple across sectors, reshaping possibilities and redefining precision.

References

Cina, A. and M. Piras (2015). Performance of low-cost GNSS receiver for landslides monitoring: Test and results, Geomatics, Natural Hazards and Risk, 6(5-7): 497-514. 

Dabove, P., V. Di Pietra and M. Piras (2020). GNSS Positioning Using Mobile Devices with the Android Operating System, ISPRS International Journal of Geo-Information, 9(4): 220. 

EGSA (European Global Navigation Satellite Systems Agency) (2019). GNSS Market Report. Issue 6; Retrieved from https://www.gsa.europa.eu/market/market-report; accessed on 20 April, 2020. 

Marchi, M. D., Diantini A. and Pappalardo, S.E. (2022). Drones and Geographical Information Technologies in Agroecology and Organic Farming. CRC Press.

Suggestion for Citation:
Amerudin, S. (2023). Pioneering High-Accuracy GNSS Positioning with Low-Cost Receivers and Smartphones. [Online] Available at: https://people.utm.my/shahabuddin/?p=6611 (Accessed: 14 August 2023).
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Sub-Meter Accuracy in Consumer Smartphones: Advancements and Challenges in GNSS Positioning

By Shahabuddin Amerudin

Source: https://blog.junipersys.com

Sub-meter accuracy in the context of GNSS (Global Navigation Satellite System) refers to the capability of a receiver to determine its position with an accuracy of less than one meter, typically in the range of centimeters or decimeters. This level of accuracy is highly desirable for various applications, including augmented reality, precise navigation, surveying, agriculture, and other location-based services where high precision is crucial.

To achieve sub-meter accuracy, GNSS receivers need access to highly accurate and precise satellite positioning data. Traditional consumer-grade GNSS receivers, such as those found in smartphones, typically provide accuracy in the range of a few meters, which is sufficient for many general navigation purposes but not suitable for applications requiring high precision.

Several factors contribute to the challenge of achieving sub-meter accuracy in consumer smartphones:

  1. Limited Observables: Consumer smartphones typically have limited access to the satellite constellations and signals. For instance, they may receive only L1 (single frequency) signals from GPS and Galileo constellations, while multi-frequency access is limited to only certain satellites.
  2. Noisy Environment: Smartphones have compact designs, and their GNSS antennas are often shared with other communication hardware like Bluetooth and Wi-Fi receivers. This setup leads to noisy electromagnetic interference, reducing the signal quality from GNSS satellites.
  3. Signal Quality and Multipath: The quality of GNSS signals received by smartphones can be affected by factors like multipath, where the signals bounce off surrounding objects, causing inaccuracies in the position calculation.
  4. Real-Time Constraints: Achieving sub-meter accuracy in real-time is more challenging than post-processing data after collection. Real-time positioning requires fast and efficient algorithms that can handle the limited observations and noisy environment of smartphones.

To address these challenges and achieve sub-meter accuracy, researchers and developers have been working on innovative methodologies and algorithms. Some of the techniques used to improve accuracy in consumer smartphones include:

  1. Precise Point Positioning (PPP): PPP is a GNSS positioning technique that can achieve high accuracy by using advanced mathematical models to account for ionospheric and tropospheric errors. By combining dual-frequency measurements when available and using real-time ionospheric models, PPP can enhance the accuracy of single-frequency observations.
  2. SFDF Combination: Single-Frequency Dual-Frequency (SFDF) combination is a method to combine L1 (single frequency) and L5 (dual-frequency) observations in smartphones. This technique compensates for the lack of dual-frequency measurements in most satellites, improving the accuracy of positioning results.
  3. Signal-to-Noise Ratio (SNR) Weighting: SNR is a measure of the signal quality received by the smartphone’s GNSS receiver. Applying SNR weighting in the positioning algorithm can help filter out poor-quality measurements and improve the overall accuracy.
  4. Pre-processing Filters: Implementing pre-processing filters to remove poor-quality observations caused by multipath or other interference can enhance the accuracy of the final position solution.
  5. Real-Time Corrections: Accessing real-time corrections from GNSS augmentation systems or base stations can significantly improve the accuracy of the positioning results.

Despite the challenges, ongoing research and advancements in GNSS technology have made it possible to achieve sub-meter accuracy on consumer smartphones. By implementing sophisticated algorithms, utilizing real-time corrections, and optimizing the use of available observations, smartphone users can experience improved accuracy, making it suitable for various high-precision applications.

Suggestion for Citation:
Amerudin, S. (2023). Sub-Meter Accuracy in Consumer Smartphones: Advancements and Challenges in GNSS Positioning. [Online] Available at: https://people.utm.my/shahabuddin/?p=6588 (Accessed: 31 July 2023).
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Understanding Location Accuracy Requirements in Various Applications

By Shahabuddin Amerudin

Introduction

Location accuracy plays a vital role in numerous applications, enabling precise positioning, navigation, and tailored experiences. Different applications have varying location accuracy requirements based on their specific use cases and objectives. In this article, we explore a range of applications and their location accuracy needs, spanning from high accuracy requirements in the millimeter range to general accuracy needs within a few meters.

  1. Surveying and Mapping Applications (mm to cm accuracy): Applications used for professional surveying, cartography, or mapping often require extremely high accuracy. These applications demand location accuracy in the millimeter to centimeter range to ensure precise delineation of boundaries, topographical mapping, and engineering projects.
  2. Autonomous Vehicles (cm accuracy): Autonomous vehicles, including self-driving cars, require reliable and accurate positioning for safe navigation. Location accuracy within the centimeter range is necessary to ensure precise localization and path planning, enabling vehicles to detect obstacles and make accurate driving decisions.
  3. Augmented Reality (AR) Applications (cm to dm accuracy): AR applications that overlay virtual content on the real world require moderate accuracy for proper alignment. Location accuracy within the centimeter to decimeter range is typically sufficient to ensure virtual objects are accurately placed and aligned with the physical environment.
  4. Precision Agriculture (dm accuracy): Applications used in precision agriculture, such as crop monitoring or autonomous farming, benefit from location accuracy within the decimeter range. This level of accuracy allows for precise mapping of field conditions, targeted application of resources, and monitoring of crop health.
  5. Construction and Building Information Modeling (BIM) (dm to m accuracy): Construction and BIM applications require accurate positioning for planning and coordination. Location accuracy within the decimeter to meter range helps ensure accurate alignment of building elements, clash detection, and accurate material takeoffs.
  6. Emergency Services (m accuracy): Emergency services applications, including emergency response and disaster management systems, require location accuracy within a few meters. This level of accuracy is critical for quickly and accurately identifying the location of emergencies, coordinating response efforts, and providing timely assistance.
  7. Navigation and Routing Applications (m accuracy): Navigation and routing applications, such as turn-by-turn navigation or route planning, generally require accuracy within a few meters. This level of accuracy enables accurate guidance, real-time traffic updates, and reliable estimated time of arrival (ETA) calculations.
  8. Geolocation-Based Services (m accuracy): Geolocation-based services, including finding nearby points of interest or location-based recommendations, typically require accuracy within a few meters. This level of accuracy ensures relevant information is provided based on the user’s proximity to specific locations.
  9. Fitness and Activity Tracking Apps (m accuracy): Fitness and activity tracking apps, such as running or cycling trackers, often require accuracy within a few meters. This level of accuracy is sufficient for mapping and tracking user movements during various activities.
  10. Social Media Check-In Apps (m accuracy): Social media check-in apps rely on accurate location information to tag user posts with their current location. Accuracy within a few meters ensures that users can accurately share their location and connect with others nearby.
  11. Weather and Environmental Monitoring (m accuracy): Weather and environmental monitoring apps provide localized weather forecasts or track environmental conditions. Accuracy within a few meters helps provide accurate and location-specific weather information.
  12. Public Transportation Apps (tens of meters accuracy): Public transportation apps that provide information on bus or train schedules typically require accuracy within tens of meters. This level of accuracy ensures accurate departure and arrival information, and helps users locate nearby transit stops or stations.
  13. Real Estate and Property Apps (tens of meters accuracy): Real estate and property apps, including property search or rental platforms, benefit from accuracy within tens of meters. This level of accuracy helps users find properties in their desired location or explore nearby amenities.
  14. Location-Based Gaming Apps (tens of meters accuracy): Location-based gaming apps, such as treasure hunts or location-based challenges, generally require accuracy within tens of meters. This level of accuracy allows for precise placement of in-game elements and enhances the gaming experience by aligning virtual content with the user’s physical surroundings. However, in some cases, the required accuracy is typically within a few meters.
  15. Social Networking and Local Recommendations (tens to hundreds of meters accuracy): Social networking apps and local recommendation platforms often rely on accuracy within tens to hundreds of meters. This level of accuracy provides a general idea of the user’s location, allowing for location-based social interactions and delivering relevant recommendations based on nearby points of interest.
  16. Outdoor Recreation and Adventure Apps (tens to hundreds of meters accuracy): Outdoor recreation and adventure apps, such as hiking or trail mapping applications, generally require accuracy within tens to hundreds of meters. This level of accuracy allows users to navigate trails, find landmarks, and plan their outdoor activities effectively. However, in some cases, the required accuracy is typically within a few meters.
  17. Delivery and Logistics Apps (tens to hundreds of meters accuracy): Delivery and logistics apps, including package tracking or fleet management systems, typically require accuracy within tens to hundreds of meters. This level of accuracy enables efficient route planning, real-time tracking of shipments or vehicles, and effective management of logistics operations. However, in some cases, the required accuracy is typically within a few meters.
  18. Field Data Collection and Surveys (tens to hundreds of meters accuracy): Field data collection apps used for surveys, research, or asset management purposes generally require accuracy within tens to hundreds of meters. This level of accuracy allows for effective mapping and data collection, providing valuable insights for various industries and research projects. However, in some cases, the required accuracy is typically within a few meters.
  19. Location-Based Attendance and Access Control (tens to hundreds of meters accuracy): Applications used for attendance tracking, access control systems, or workforce management often require accuracy within tens to hundreds of meters. This level of accuracy allows for efficient monitoring of personnel and assets within designated areas.
  20. IoT (Internet of Things) and Asset Tracking (tens to hundreds of meters accuracy): IoT applications and asset tracking systems that monitor the location of objects or assets typically require accuracy within tens to hundreds of meters. This level of accuracy is sufficient for general tracking and management of assets across various industries. However, in some cases, the required accuracy is typically within a few meters.

Conclusion

It’s important to note that these accuracy ranges are general guidelines and can vary depending on specific application requirements and user expectations. Additionally, advancements in technology, such as the availability of higher-quality GNSS receivers or the integration of sensor fusion techniques, may further improve location accuracy in various applications.

Developers should consider the specific needs of their applications and strike a balance between the required accuracy and the available resources and technologies. It’s also important to inform users about the expected accuracy level and manage their expectations to ensure a satisfactory user experience.

Advancements in technology and the ongoing development of positioning techniques are expected to further improve location accuracy across various applications, allowing for more precise and tailored experiences in the future.

Suggestion for Citation:
Amerudin, S. (2023). Understanding Location Accuracy Requirements in Various Applications. [Online] Available at: https://people.utm.my/shahabuddin/?p=6530 (Accessed: 6 July 2023).
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Understanding Location Accuracy in Consumer Smartphones

By Shahabuddin Amerudin

Introduction

Consumer smartphones have become integral to our daily lives, offering a wide range of features and functionalities, including location-based services. The accuracy of location information provided by smartphones is crucial for navigation, mapping, and various location-dependent applications. In this article, we explore the general capabilities of consumer smartphones in achieving location accuracy and the factors that influence it.

GPS Technology and Accuracy

Global Positioning System (GPS) is a widely used positioning technology in smartphones. With high-quality GPS receivers, smartphones can achieve location accuracy within a few meters under ideal conditions. GPS relies on signals from satellites orbiting the Earth to determine precise location coordinates.

GNSS Capabilities

Many modern smartphones support multiple Global Navigation Satellite Systems (GNSS), including GPS, GLONASS, Galileo, and BeiDou. GNSS-capable smartphones have the advantage of accessing signals from multiple satellite constellations, enhancing location accuracy. By leveraging a combination of satellite signals, smartphones can achieve improved accuracy and reliability, particularly in challenging environments.

Assisted Positioning Techniques

Smartphones often employ assisted positioning techniques such as Assisted GPS (A-GPS) or Assisted GLONASS (A-GLONASS). These techniques leverage cellular networks or Wi-Fi data to assist in determining the user’s location. By utilizing additional data sources, smartphones can enhance positioning speed and accuracy, especially in urban environments or when GPS signals are weak or obstructed.

Sensor Fusion for Improved Accuracy

Sensor fusion technologies play a crucial role in enhancing location accuracy. By integrating GPS data with information from other sensors like accelerometers, gyroscopes, or magnetometers, smartphones can improve accuracy and stability. Sensor fusion allows smartphones to compensate for temporary signal loss, obstructions, or other limitations, resulting in more reliable location information.

Network-Based Positioning

In addition to satellite-based positioning, smartphones can utilize network-based methods such as Wi-Fi positioning or cell tower triangulation. When GPS signals are weak or unavailable, these techniques estimate the user’s location based on Wi-Fi network information or signals from nearby cell towers. While network-based positioning provides coarser accuracy within tens to hundreds of meters, it serves as a valuable backup when satellite signals are limited.

Factors Affecting Location Accuracy

Location accuracy in smartphones can vary due to various external factors. The availability of satellite signals, environmental conditions, signal interference, and the specific hardware and software capabilities of the smartphone all influence accuracy. Additionally, smartphone manufacturers may employ proprietary technologies or algorithms to optimize location accuracy in their devices, resulting in varying performance across different models.

Determining Location Accuracy

To determine the precise location accuracy of a particular smartphone model, it is best to refer to the specifications provided by the manufacturer. However, independent tests and reviews that evaluate the device’s performance in real-world scenarios can provide valuable insights. These tests assess factors such as accuracy under different conditions, signal acquisition time, and performance in challenging environments.

Conclusion

Consumer smartphones on the market today offer varying levels of location accuracy, ranging from a few meters to sub-meter accuracy under optimal conditions. By leveraging GPS, GNSS capabilities, assisted positioning, sensor fusion, and network-based methods, smartphones strive to provide accurate location information. However, it’s important to consider external factors and individual device capabilities that can impact accuracy. Regular advancements in smartphone technology continue to enhance location accuracy, contributing to improved user experiences and the growth of location-based applications in our daily lives.

Suggestion for Citation:
Amerudin, S. (2023). Understanding Location Accuracy in Consumer Smartphones. [Online] Available at: https://people.utm.my/shahabuddin/?p=6527 (Accessed: 6 July 2023).
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Understanding Location Inaccuracy in Mapping Applications

By Shahabuddin Amerudin

Shown GPS Location on the System
User’s Real Position on Site

Introduction

Location accuracy plays a crucial role in mapping applications, allowing users to navigate, find points of interest, and track their movements. However, it’s important to acknowledge that location inaccuracy can sometimes occur, leading to discrepancies between the displayed location and the user’s actual position. In this article, we explore the various factors that contribute to location inaccuracy and discuss strategies to mitigate its impact on mapping applications.

GPS Accuracy

GPS (Global Positioning System) relies on satellite signals to determine precise location coordinates. However, several factors can affect the accuracy of GPS signals. Device limitations, such as lower-quality GPS receivers, can result in less accurate location readings. Environmental conditions, like dense urban areas or deep indoor environments, can weaken GPS signals or introduce multipath interference, leading to inaccuracies. Additionally, the number and positioning of visible satellites at any given time can impact the accuracy of GPS readings.

Geolocation Errors

Mapping applications often rely on geolocation APIs to retrieve the user’s position. However, these APIs can introduce errors or inaccuracies in the reported location. The accuracy value provided by geolocation APIs may not always reflect the true error in the location estimation. Factors such as signal noise, limited sensor data, or the interpolation of location data can contribute to discrepancies between the reported location and the user’s actual position.

Network and Signal Interference

In scenarios where GPS signals are weak or unavailable, mapping applications may fall back on network-based positioning methods like Wi-Fi or cell tower triangulation. However, network connectivity issues or signal interference can affect the accuracy of these methods. Unreliable or spotty Wi-Fi networks, for example, may introduce inaccuracies in determining the user’s location. Similarly, obstacles or environmental conditions can interfere with the strength and quality of cellular signals, impacting the accuracy of cell tower triangulation.

User Permissions

To access precise location information, users need to grant location permissions to mapping applications. If users do not grant precise location permissions or disable location services on their devices, geolocation APIs may resort to less accurate positioning methods. This fallback mechanism, while providing approximate location data, can introduce additional inaccuracies compared to when precise location permissions are granted.

Device or Browser Limitations

Location accuracy can also be influenced by the device or browser being used. Different devices and browsers may have varying levels of geolocation capabilities and support for high-precision GPS. Older devices or browsers may lack advanced positioning technologies or have less accurate GPS receivers, leading to decreased location accuracy. It’s important for developers to consider these limitations when building mapping applications.

Mitigating Location Inaccuracy

While achieving perfect location accuracy in all scenarios is challenging, there are strategies developers can employ to improve the accuracy of displayed location in mapping applications:

  • Encouraging users to be in open areas: Advise users to be in open spaces with a clear view of the sky whenever possible. This can enhance GPS signal strength and reduce obstructions that may lead to inaccuracies.
  • Informing users about limitations: Set appropriate user expectations by providing information about the potential factors that can affect location accuracy. Educating users about the variability of accuracy readings can help manage their expectations.
  • Implementing error handling: Develop robust error handling mechanisms to handle cases where location accuracy is low or undetermined. Inform users when the accuracy falls below a certain threshold and provide appropriate feedback to avoid misleading information.
  • Considering additional positioning methods or APIs: Explore alternative positioning methods or APIs that can complement GPS data. Combining GPS with Wi-Fi or cellular network information can improve accuracy in urban areas or when GPS signals are weak.
  • Regularly updating applications and libraries: Stay up-to-date with updates and bug fixes related to geolocation functionality. Regularly check for new releases of libraries or APIs used in the mapping application to benefit from improvements that can enhance location accuracy.

Conclusion

Location inaccuracy in mapping applications can occur due to various factors, including GPS limitations, geolocation errors, network and signal interference, user permissions, and device/browser limitations. While efforts can be made to improve location accuracy, achieving pinpoint accuracy in all scenarios may not always be possible due to external factors and limitations.

By understanding the factors that contribute to location inaccuracy and implementing strategies to mitigate its impact, developers can enhance the user experience and provide more reliable location information. It is essential to manage user expectations, provide accurate error handling, and explore alternative positioning methods or APIs when necessary.

As technology continues to advance and new positioning techniques emerge, ongoing research and development efforts aim to improve location accuracy in mapping applications. By staying informed and adapting to advancements in geolocation technology, developers can strive for increasingly accurate and reliable location data in their applications.

Suggestion for Citation:
Amerudin, S. (2023). Understanding Location Inaccuracy in Mapping Applications. [Online] Available at: https://people.utm.my/shahabuddin/?p=6523 (Accessed: 6 July 2023).
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Location Privacy

By Shahabuddin Amerudin

Location privacy has become a growing concern in the digital age, with individuals and organizations alike expressing unease about the collection, use, and storage of location data. Beresford and Stajano (2003) define location privacy as “the ability to prevent other parties from learning one’s current or past location,” emphasizing the importance of individual control over location data.

Modern devices use various methods to determine a user’s location, including satellite navigation systems, mobile carrier antennas, and wireless networks (Zhang et al., 2020). The use of multiple methods can improve positioning accuracy but may consume more power and reduce battery life.

Satellite navigation systems, such as the Global Positioning System (GPS), are commonly used to determine location outdoors, with high levels of accuracy of just a few centimeters (European Space Agency, 2016). However, accuracy can decrease in areas with signal obstruction, such as inside buildings, and current free public access has an accuracy of approximately one-meter radius (Dardari et al., 2015).

Despite the benefits of location-based services, including personalized content and targeted advertising, concerns about the potential risks associated with location data collection and use have grown in recent years. Location data can reveal sensitive information about a user’s daily routines and habits, and can even be used to track individuals’ movements by malicious actors, such as stalkers or burglars.

To address these concerns, various measures can be taken to protect location privacy. One such measure is limiting the collection of location data by disabling location services for apps that do not require it. This can be done through device settings, allowing users to control which apps have access to location data.

Another option is to use privacy-focused apps that do not collect or store location data. These apps often use alternative methods, such as IP address tracking, to provide personalized content and services without collecting sensitive location data.

Virtual private networks (VPNs) can also be used to protect location privacy by encrypting internet traffic and masking a user’s location. This can be particularly useful when using public Wi-Fi networks, which are vulnerable to hacking and surveillance.

In conclusion, location privacy is a critical aspect of modern privacy rights, and individuals and organizations should take measures to protect their location data. By limiting the collection of location data, using privacy-focused apps, and using VPNs, users can maintain greater control over their location information and reduce the risks associated with location data collection and use.

References:

Beresford, A. R., & Stajano, F. (2003). Location Privacy in Pervasive Computing. IEEE Pervasive Computing, 2(1), 46-55. https://doi.org/10.1109/MPRV.2003.1186726

Dardari, D., Closas, P., Djurić, P. M., & Vatalaro, F. (2015). Indoor Tracking: Theory, Methods, and Technologies. CRC Press.

European Space Agency. (2016). Galileo – System Status. https://www.gsc-europa.eu/system-status/gps-galileo-status

Zhang, T., Han, J., & Zhang, S. (2020). An Improved Method for Indoor Localization Based on Wireless Networks. IEEE Access, 8, 21248-21256. https://doi.org/10.1109/ACCESS.2020.2965056

Suggestion for Citation:
Amerudin, S. (2023). Location Privacy. [Online] Available at: https://people.utm.my/shahabuddin/?p=6285 (Accessed: 9 April 2023).
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Understanding GDM2000: The Geodetic Datum that Powers Malaysia’s Geospatial Data

Have you ever wondered how maps, GPS devices, and other location-based technologies work? How they’re able to tell you exactly where you are and how to get where you’re going? The answer lies in a concept known as geodetic datums.

A geodetic datum is a set of reference points and parameters that are used to define the shape and position of the Earth’s surface. These datums are used in a wide range of applications, including mapping, surveying, navigation, and Geographic Information Systems (GIS).

In Malaysia, the official geodetic datum is called GDM2000 (Geodetic Datum of Malaysia 2000). It was adopted in 2002 to replace the older Kertau 1968 datum, which was based on measurements taken from a single station in Kertau, Pahang.

GDM2000 is a modern and accurate geodetic datum that is based on a network of 37 GPS stations distributed throughout Peninsular Malaysia, Sabah, and Sarawak. These stations were observed over a period of several years to determine their precise positions, which were then used to define the datum.

So why is GDM2000 important? Well, it provides a standard reference system for all geospatial data in Malaysia, including maps, charts, and GPS coordinates. This means that different organizations and agencies can use the same reference system, which helps to ensure consistency and accuracy in their data.

GDM2000 is also used for surveying, engineering, and construction projects, as well as for GIS and remote sensing applications. For example, if you’re building a new road or bridge, you need to know the exact location of the site and the elevations of the surrounding terrain. GDM2000 provides this information in a standardized format that can be used by engineers and construction crews.

Similarly, if you’re managing natural resources or monitoring environmental conditions, you need to know the exact locations of the resources or the monitoring sites. GDM2000 provides this information in a standardized format that can be used by resource managers and environmental scientists.

But how does GDM2000 compare to other geodetic datums, such as WGS84 (World Geodetic System 1984)? Well, both datums are used to define positions on the Earth’s surface, but they have some differences in terms of their regional coverage and the reference ellipsoids they use.

GDM2000 is a regional datum that is optimized for use in Malaysia and the surrounding regions. It uses the Malaysian ellipsoid to define the shape of the Earth’s surface, which is based on measurements taken from the GPS stations in the network. WGS84, on the other hand, is a global datum that is used by many different countries and organizations around the world. It uses a global ellipsoid that is based on the best available measurements from around the world.

The differences between GDM2000 and WGS84 can vary depending on the location and the method of measurement. In Malaysia, the differences between the two datums can range from a few centimeters to a few meters, depending on the location and the specific method of measurement.

To minimize the differences between the two datums, it’s important to use appropriate transformation parameters when converting between GDM2000 and WGS84 coordinates. These parameters are typically provided by the Department of Survey and Mapping Malaysia (JUPEM) and are based on a network of control points that have been measured using both datums.

Overall, GDM2000 is an important geodetic datum that plays a crucial role in many aspects of modern life in Malaysia. From navigation and mapping to engineering and resource management, GDM2000 provides a standardized reference system that helps to ensure accuracy and consistency in geospatial data.

One of the key benefits of GDM2000 is that it is based on a modern and accurate network of GPS stations that have been carefully surveyed and measured to determine their precise positions. This means that GDM2000 is able to provide a high degree of accuracy and precision in geospatial data, which is essential for many applications.

Another benefit of GDM2000 is that it is optimized for use in Malaysia and the surrounding regions, which means that it provides a more accurate and reliable reference system than global datums like WGS84. While WGS84 is widely used around the world, it may not be the most accurate or appropriate reference system for every location and application.

Of course, there are some limitations and challenges associated with using GDM2000. One of the biggest challenges is ensuring that all data and measurements are consistent and compatible with the datum. This requires careful attention to detail and rigorous quality control procedures to ensure that data is accurate and consistent.

Another challenge is managing the differences between GDM2000 and other geodetic datums, particularly WGS84. While the differences between the two datums are relatively small, they can still have a significant impact on the accuracy and precision of geospatial data. This is why it’s important to use appropriate transformation parameters when converting between the two datums.

Despite these challenges, GDM2000 remains an essential and valuable tool for many different applications in Malaysia. Whether you’re navigating through the city streets, building a new road or bridge, or monitoring environmental conditions, GDM2000 provides a reliable and standardized reference system that helps to ensure accuracy and consistency in geospatial data.

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Unlocking the Power of Spatial Data: The Vital Role of Positioning, Location, Surveying, and Measurement in GIS

Introduction

Positioning and location are two key concepts in the field of geography and Geographic Information Systems (GIS). These concepts are closely related to surveying and measurement, which are essential for determining the exact location of points and objects in space. GIS technology enables users to capture, store, manage, analyze, and display spatial data, which makes it a critical tool for many fields, including urban planning, transportation, environmental management, and disaster management. This article will explain the relationship between positioning, location, surveying, and measurement and their application in GIS.

Positioning and Location

Positioning refers to the process of determining the location of an object or point relative to a specific reference point or coordinate system. The position of an object can be determined using various techniques, including GPS (Global Positioning System), which uses satellites to determine the exact location of an object. The location of an object, on the other hand, is the specific geographic coordinates or address of a point or object. Location can be expressed in terms of latitude and longitude, street address, or other geographic references.

Surveying and Measurement

Surveying is the process of measuring and mapping the Earth’s surface, typically for construction, engineering, or land use purposes. Surveyors use a variety of tools and techniques to determine the exact location and elevation of points on the ground, including total stations, GPS receivers, and laser scanners. Measurement is the process of quantifying a physical property, such as length, width, height, or distance. In the context of positioning and surveying, measurement is used to determine the exact location and orientation of objects or points relative to a reference system.

Relationship between Positioning, Location, Surveying, and Measurement

Positioning and location are closely related to surveying and measurement, as these concepts are used to accurately determine the location and orientation of objects and points in space. Surveying is used to create accurate maps and models of the Earth’s surface, while measurement is used to determine distances, areas, and volumes. These measurements can be critical for planning and decision-making in a wide range of fields, including urban planning, transportation, and disaster management.

GIS and Positioning, Location, Surveying, and Measurement

GIS is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. GIS integrates these concepts into its functions and provides an efficient way to handle and analyze spatial data. By accurately measuring and determining the location of points and objects in space, GIS can better understand and utilize the physical world around us.

GIS technology enables users to capture, store, manage, analyze, and display spatial data, making it a critical tool for many fields, including urban planning, transportation, environmental management, and disaster management. In urban planning, GIS technology can be used to determine the location of buildings, roads, and other infrastructure. In transportation, GIS technology can be used to plan routes, optimize logistics, and manage traffic. In environmental management, GIS technology can be used to monitor changes in ecosystems and plan for sustainable land use. In disaster management, GIS technology can be used to assess the impact of natural disasters and plan for recovery efforts.

Applications of Positioning, Location, Surveying, and Measurement in GIS

Positioning, location, surveying, and measurement are commonly used in GIS for a wide range of applications. In this section, we will explore some of the common applications of these concepts in GIS.

  1. Urban Planning GIS technology is used in urban planning to create accurate maps and models of the built environment. Surveying and measurement are used to determine the location and orientation of buildings, roads, and other infrastructure. This information can be used to plan and design urban areas that are efficient, safe, and sustainable. GIS can also be used to analyze the impact of proposed developments on the existing infrastructure and environment.
  2. Transportation GIS technology is used in transportation to plan routes, optimize logistics, and manage traffic. Surveying and measurement are used to determine the location and orientation of roads, bridges, and other transportation infrastructure. This information can be used to optimize traffic flow, reduce congestion, and improve safety.
  3. Environmental Management GIS technology is used in environmental management to monitor changes in ecosystems and plan for sustainable land use. Surveying and measurement are used to determine the location and orientation of natural features such as forests, wetlands, and wildlife habitats. This information can be used to develop strategies for conservation and sustainable land use.
  4. Disaster Management GIS technology is used in disaster management to assess the impact of natural disasters and plan for recovery efforts. Surveying and measurement are used to determine the location and extent of damage caused by natural disasters such as earthquakes, floods, and wildfires. This information can be used to prioritize recovery efforts and allocate resources effectively.

Conclusion

Positioning, location, surveying, and measurement are critical concepts in GIS and are used in a wide range of applications, including urban planning, transportation, environmental management, and disaster management. GIS technology enables users to capture, store, manage, analyze, and display spatial data, making it a powerful tool for understanding and utilizing the physical world around us. By accurately measuring and determining the location of points and objects in space, GIS can help us make better decisions about how to manage our environment and resources.

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Assisted Global Positioning System (AGPS)

Assisted Global Positioning System (AGPS) is a technology used to enhance the performance of the Global Positioning System (GPS) by utilizing information from other sources, such as cellular networks and satellite-based augmentation systems, to provide faster and more accurate location information.

GPS is a satellite-based navigation system that provides location and time information to receivers on the ground or in the air. However, GPS signals can be blocked by buildings, trees, or other obstacles, resulting in reduced accuracy or even complete signal loss. AGPS overcomes this limitation by utilizing information from cellular networks to supplement GPS signals.

AGPS works by using a combination of GPS signals and information from cellular towers to calculate a more accurate location. When a GPS-enabled device (such as a smartphone) is turned on, it sends a request to the nearest cell tower for assistance in obtaining a GPS fix. The cell tower then sends the device a reference signal that includes the location of the tower and the current time.

The device uses this information to calculate its distance from the tower, which helps to narrow down its position. The GPS receiver on the device then uses this information to acquire GPS signals from satellites more quickly and accurately. By combining the GPS signals with the reference signal from the cell tower, AGPS is able to provide faster and more accurate location information than GPS alone.

AGPS also helps to conserve battery life on GPS-enabled devices by reducing the time it takes to obtain a GPS fix. By utilizing the reference signal from the cell tower, the GPS receiver can acquire GPS signals more quickly and accurately, which reduces the amount of time and energy required to maintain a GPS fix.

AGPS is commonly used in smartphones, tablets, and other GPS-enabled devices. It has become a popular technology due to its ability to improve GPS performance in areas with poor satellite reception and its ability to conserve battery life. However, AGPS is not a replacement for GPS and still relies on GPS signals for accurate location information.

In addition to utilizing cellular networks, AGPS can also make use of other satellite-based augmentation systems (SBAS) such as the Wide Area Augmentation System (WAAS) and the European Geostationary Navigation Overlay Service (EGNOS). These systems transmit correction data to GPS receivers that enable them to calculate more accurate positions. AGPS can use these corrections to further improve the accuracy of location information.

AGPS can also make use of network-assisted GPS (NAGPS), which uses information from both cellular networks and Wi-Fi networks to supplement GPS signals. NAGPS can provide even faster and more accurate location information than AGPS alone.

There are several benefits to using AGPS over traditional GPS. AGPS can provide faster and more accurate location information, which can be especially useful in urban areas or areas with poor satellite reception. It can also conserve battery life on GPS-enabled devices, making it a popular technology in smartphones and other mobile devices.

However, there are some limitations to AGPS. It requires a cellular network connection, which may not be available in all areas. Additionally, AGPS may not be as accurate as traditional GPS in areas with weak or no cellular network coverage. Finally, AGPS can be more expensive to implement than traditional GPS due to the need for additional infrastructure and technology.

Overall, AGPS is a valuable technology that can provide faster and more accurate location information for GPS-enabled devices. It has become a popular technology in smartphones and other mobile devices, and is likely to continue to be an important technology in the future.

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How Apps Detect A User’s Location

By Shahabuddin Amerudin

There are several ways that apps can detect a user’s location. The most common methods are:

  • GPS (Global Positioning System) – GPS is a satellite-based system that uses a network of satellites to determine the user’s location. GPS-enabled devices, such as smartphones, can access this system and use the information to determine the user’s location. The device uses multiple satellite signals to triangulate its location, and this process is called trilateration. The device calculates the distance to each satellite by measuring the time it takes for a signal to travel from the satellite to the device. By measuring the distance to multiple satellites, the device can determine its location with high accuracy.
  • A-GPS (Assisted GPS) – A-GPS is a hybrid system that combines GPS with other location-detection methods, such as WiFi and cell tower triangulation. A-GPS can improve the accuracy and speed of location detection, particularly in urban areas where GPS signals may be weak.
  • WiFi-based Location Detection – WiFi-based location detection uses the signals from nearby WiFi networks to determine the user’s location. The device scans for available WiFi networks and compares the MAC addresses of the networks to a database of known networks and their corresponding locations. This method can be more accurate than GPS in certain situations, such as indoor locations where GPS signals may be weak.
  • Cell Tower Triangulation – Cell tower triangulation uses the signals from nearby cell towers to determine the user’s location. The device uses the signal strength and timing of the signals from multiple cell towers to triangulate its location. This method can be less accurate than GPS, but it can be useful in areas where GPS signals may be weak.
  • IP Geolocation – IP geolocation uses the IP address of the device to determine the user’s location. This method can be less accurate than GPS or WiFi-based location detection, but it can be useful in situations where the device does not have GPS or WiFi capabilities.
  • Bluetooth-based Location Detection – Bluetooth-based location detection uses the signals from nearby Bluetooth devices to determine the user’s location. The device scans for available Bluetooth devices and compares the MAC addresses of the devices to a database of known devices and their corresponding locations. This method can be useful for indoor location detection and it’s less power consuming compared to GPS or WiFi-based location detection.

It’s worth noting that apps usually use a combination of these methods, and they often have fallback methods in case one method fails. For example, if GPS signals are weak, the app may switch to WiFi-based location detection or cell tower triangulation. Developers also need to consider the user’s privacy and security when it comes to location detection and they must comply with the laws and regulations of each country.

The accuracy of location detection methods can vary depending on several factors, such as the device and its location, the environment, and the methods used.

  • GPS is generally considered the most accurate method of location detection, providing location information to within a few meters. However, its accuracy can be affected by factors such as the number of visible satellites, the environment (e.g. tall buildings, trees, or heavy cloud cover can block or weaken GPS signals), and interference from other sources.
  • A-GPS, which combines GPS with other location-detection methods, can improve the accuracy and speed of location detection, particularly in urban areas where GPS signals may be weak. However, it still relies on GPS signals and can be affected by the same factors that affect GPS accuracy.
  • WiFi-based location detection can be more accurate than GPS in certain situations, such as indoor locations where GPS signals may be weak. However, its accuracy depends on the availability and accuracy of the database of known WiFi networks and their corresponding locations.
  • Cell tower triangulation can be less accurate than GPS, but it can be useful in areas where GPS signals may be weak. Its accuracy depends on the density of cell towers in the area and the quality of the signals from the towers.
  • IP geolocation can be less accurate than GPS or WiFi-based location detection, but it can be useful in situations where the device does not have GPS or WiFi capabilities. Its accuracy depends on the quality of the IP address to location mapping database.
  • Bluetooth-based location detection can be useful for indoor location detection, it is less power consuming compared to GPS or WiFi-based location detection. However, its accuracy depends on the availability and accuracy of the database of known Bluetooth devices and their corresponding locations.

Overall, it’s important to note that the accuracy of location detection methods can vary depending on the device and its location, the environment, and the methods used. Developers need to take these factors into consideration when designing location-based applications and users should be aware of the potential limitations and inaccuracies of these methods. Additionally, privacy concerns should be considered when using location-based services, as the collection and use of location data can pose risks to personal privacy.

Suggestion for Citation:
Amerudin, S. (2023). How Apps Detect A User's Location. [Online] Available at: https://people.utm.my/shahabuddin/?p=5762 (Accessed: 23 January 2023).
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GNSS for Underwater

Source: https://www.mdpi.com/2072-4292/12/21/3636/htm

The optimal arrangement of buoys is determined in terms of accuracy and availability depending on the number of available buoys and targeted water depth for an efficient operation. For example, at a targeted depth of 30 m in fresh water, a maximum horizontal position root-mean-square (RMS) error of less than 3 m can be achieved with a set of five buoys providing a service radius of up to 72 m.

https://doi.org/10.3390/rs12213636

The formal transfer of GNSS under water is not possible. The radio signals don’t reach very far underwater, they’re basically useless for any submarine below the surface. If it is work underwater GNSS transmission is not feasible. The radio waves don’t go very far underwater, thus any submarine below the surface is effectively blind. If you have to work underwater, make sure it isn’t too deep. At around 1 foot deep, you’ve already lost roughly 15dB of signal. In the open sky, you can lose roughly 25–30dB of usual signal strength and get a GPS lock.

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Location Privacy

Beresford and Stajano define location privacy as “the ability to prevent other parties from learning one’s current or past location.” This definition specifies that the data subject—or the person whose location data is affected—should have control over the use and subsequent use of their data, which can be viewed as an extension of the previously presented definition of privacy.

The device’s physical location can be determined using one of three methods: (1) satellite navigation systems like the Global Positioning System (GPS), (2) mobile carrier antennas, and (3) wireless networks.

A device can use any combination of these three methods to determine its most accurate location. A combination of methods improves positioning accuracy but consumes more power, reducing the device’s battery life (Zhang et. al., 2020).

When the equipment to be identified is outside, satellite navigation systems, which provide a mechanism for autonomous geographical location, allow positions to be calculated with an accuracy of just a few centimetres (European Space Agency, 2016). The accuracy for free access public use is presently approximately one metre radius (Dardari et. al., 2015). When the signal is obstructed by obstacles such as mountains or buildings, the accuracy falls, and it lowers dramatically in inside conditions (Dardari et. al., 2015).

Sources: Maija Elina Poikela (2020). Perceived Privacy in Location-Based Mobile System. https://doi.org/10.1007/978-3-030-34171-8

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Location-Based Service (LBS)

A Location-Based Service (LBS) is usually a service running on a mobile device that provides facts or recreational information. It employs geolocation to make the facts or entertainment more personal to the user of the application. An example of a typical LBS is one that identifies the location of a device and then discovers the location of restaurants in the immediate vicinity of that location. As LBS become more common, their commercial value will become more readily evident to corporations, who can use them to personalize users’ experiences with location-aware weather, coupons, and advertising. This is already becoming more common, and will only continue to grow in the future.

An LBS begins by gathering a location for the device using one of its available methods, which could be through GPS, the GSM/CDMA Cell ID, or its IP Address, for example. Once it has a location in latitudinal and longitudinal coordinates, it can then retrieve whatever additional information it is programmed to receive. This information is then presented to the user, most likely to be interacted with in some fashion.

Some popular examples of LBS are:

  • Turn-by-turn navigation to an inputted address
  • Notifications regarding traffic congestion or accidents
  • Location of nearby businesses, restaurants, or other services
  • Social interaction with other people nearby
  • Safety applications for tracking members of a family

This list could go on and on, as there are countless things to be done with LBS today. LBS is a large part of geolocation today, but they are not the only services that use geolocation for their functionality.

Source: Holdener (2011). HTML5 Geolocation. O’Reilly Media, Inc.

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Geocoding

Geocoding (geographically enabling unit records) is defined as the process of finding associated geographic coordinates (expressed in latitude and longitude) from other geographic data for the statistical units, such as street addresses or postal codes. (Geocoding is a way to ensure that the data “knows” where it is.)
In other words, geocoding involves taking location information for these statistical units (such as address) and linking this information to a location coordinate (i.e., x,y,z coordinates) and/or a small geographic area. The geocodes (the location coordinates and geographic areas codes) obtained from this process can be stored directly on the statistical unit record or linked in some way to the record. There is a common misunderstanding between geocoding and georeferencing, so it is important to emphasize that while they are related, they are quite different. Georeferencing is often done, for example, with raster images. Georeferencing is the process of referencing data against a known geospatial coordinate system by matching to known points of reference in the coordinate system so that the data can be analyzed, viewed, and queried with other geographic data.

In the GIS industry, geocoding is synonymous with address matching, which is the process of assigning map coordinate locations to addresses in a database.30 A GIS is capable of doing this by comparing the elements of an address or a table of addresses with the address attributes of a reference dataset—the GIS data layer used as the geographic reference layer (e.g., a city’s street centerlines layer)—to find a match (i.e., to determine whether particular address falls within an address range associated with a feature in the reference).

But the concept of geocoding goes beyond address matching. It covers a continuum of spatial scales: from individual housing units to EA levels, up to higher administrative or national levels. The use of GPS, directly capturing precise data at the level of point locations (latitude and longitude coordinates), allows the coding of centroids, building corners, or building point-of-entry coordinates for a unit such as a block of land, building, or dwelling.

Excerpt From: Amor Laaribi. “GIS and the 2020 Census.” iBooks.