Tag: GNSS

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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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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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Unleashing Sub-Meter Accuracy with L1 and L5 GNSS Frequencies

By Shahabuddin Amerudin

Introduction

In the realm of modern navigation and geospatial technology, achieving unparalleled accuracy has become a driving force. As industries and individuals seek to unlock the potential of location-based services, the fusion of advanced GNSS technology with smartphones and external receivers has become a game-changer. One of the pivotal benchmarks in this quest is the pursuit of sub-meter accuracy – a feat that was once confined to specialized equipment but is now within reach through innovative approaches.

The confluence of two significant GNSS frequency bands, L1 and L5, stands as a beacon of hope for realizing sub-meter accuracy. These bands, each with its unique advantages, offer the promise of enhanced precision by addressing challenges like ionospheric delays and multipath interference. In this article, we embark on a journey to explore how the strategic combination of L1 and L5 frequencies, harnessed through dual-frequency GNSS receivers, holds the key to unlocking sub-meter accuracy. We delve into the science behind the synergy and the tangible benefits it brings to a range of applications that demand precision.

Let’s delve into how this can be accomplished:

  1. L1 and L5 Bands Overview
    • L1 Band (1575.42 MHz): This is the primary frequency used by most GNSS constellations (GPS, Galileo, BeiDou, and QZSS). L1 provides standard positioning accuracy, typically ranging from a few meters to around a meter.
    • L5 Band (1176.45 MHz): The L5 frequency is designed to improve accuracy and reliability. It is used by some modernized GNSS systems, such as GPS L5 and Galileo E5a. L5 offers better resistance to ionospheric disturbances and multipath interference, contributing to enhanced accuracy.
  2. Combining L1 and L5 for Sub-Meter Accuracy
    • Ionospheric Correction: The L5 band’s resistance to ionospheric delays is a key advantage. Ionospheric delays can cause inaccuracies in positioning, especially for signals in the L1 band. By combining L1 and L5 measurements, you can mitigate the effects of ionospheric delays and improve overall accuracy.
    • Multipath Reduction: Multipath interference occurs when GNSS signals reflect off surfaces before reaching the receiver. The L5 band’s design helps reduce multipath effects, contributing to more accurate positioning.
  3. Dual-Frequency Receivers
    • Achieving sub-meter accuracy with L1 and L5 involves using dual-frequency GNSS receivers. These receivers track signals from both L1 and L5 bands simultaneously.
    • Dual-frequency receivers leverage the different signal propagation characteristics of L1 and L5 to correct for ionospheric delays and multipath effects. This leads to more accurate position solutions.
  4. RTK and Precise Point Positioning (PPP)
    • Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) techniques benefit significantly from dual-frequency GNSS receivers.
    • RTK involves receiving real-time corrections from a base station to achieve centimeter-level accuracy. Dual-frequency receivers enhance the accuracy of RTK solutions by leveraging L1 and L5 measurements.
    • PPP uses precise satellite orbit and clock data to achieve high accuracy without real-time corrections. Dual-frequency receivers improve PPP solutions by providing more accurate measurements to the positioning algorithm.
  5. Additional Considerations
    • While L1 and L5 combination enhances accuracy, the performance also depends on the overall quality of the GNSS receiver, signal environment, and the availability of augmentation services like SBAS.
    • Check whether your smartphone or external receiver supports tracking L1 and L5 signals and whether it can leverage the benefits of these frequencies for enhanced accuracy.

Conclusion

In the dynamic landscape of spatial data, the symbiosis of L1 and L5 GNSS frequency bands has ushered in a new era of precision. The pursuit of sub-meter accuracy, once a distant goal, has become a tangible reality for professionals and enthusiasts alike. By harnessing the power of dual-frequency GNSS receivers, we’ve uncovered a pathway to transform smartphones and external devices into instruments of remarkable accuracy.

As we conclude our exploration, it’s evident that the sub-meter accuracy achieved through L1 and L5 integration is not just a technical achievement, but a catalyst for innovation. It empowers sectors as diverse as surveying, agriculture, emergency response, and infrastructure management to operate with precision that was once unattainable. The convergence of technology and ingenuity, epitomized by the harmonious blend of L1 and L5 frequencies, has opened doors to possibilities that resonate across industries and applications.

In a world where accuracy is paramount, the amalgamation of L1 and L5 stands as a testament to human ingenuity’s capacity to redefine what’s achievable. As we navigate toward a future where sub-meter accuracy is the new normal, we’re reminded that the quest for precision knows no bounds – and the journey has only just begun.

Suggestion for Citation:
Amerudin, S. (2023). Unleashing Sub-Meter Accuracy with L1 and L5 GNSS Frequencies. [Online] Available at: https://people.utm.my/shahabuddin/?p=6618 (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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The Evolution of Location Based Services: A Critical Review and Analysis

By Shahabuddin Amerudin

Abstract

Location Based Services (LBS) have undergone remarkable transformations since the publication of the seminal paper “The Development of Location Based Services in Malaysia” in 2009. This paper offers an extensive review and analysis of the original article in the context of recent advancements and trends in LBS technology. By critically examining the original paper’s content, relevance, and implications for today’s technological landscape, this review aims to provide a comprehensive understanding of the evolution of LBS.

1. Introduction

The original paper, published in 2009, explored the development of LBS in Malaysia, particularly in conjunction with the National Spatial Database Infrastructure (NSDI) and the Open Location Service (OpenLS) platform. However, the last decade has witnessed exponential technological growth, reshaping the landscape of LBS and expanding its potential far beyond the scope of the original paper. In this review, we delve into the progression of LBS, analyzing how it aligns with the paper’s objectives and uncovering new perspectives.

2. Scope and Relevance

The original paper was instrumental in laying the groundwork for understanding LBS within the context of the NSDI and OpenLS. While its contribution was valuable, it was limited by the technological constraints of its time. The scope primarily centered on technological integration and the potential benefits, leaving out considerations for challenges, drawbacks, and real-world implementations. Thus, this review aims to fill the gap by examining the paper’s content through a contemporary lens.

3. Technological Evolution

3.1 Standards and APIs

The paper underscored the importance of OpenLS and SOAP-based web services as foundations for LBS. Today, RESTful APIs have gained dominance due to their lightweight communication and compatibility with modern web technologies. Moreover, standards such as GeoJSON and GeoPackage have emerged as versatile options for geospatial data exchange, offering a departure from the XML-centric approach discussed in the original paper.

3.2 Mobile Application Development

While the original paper emphasized mobile phones as the primary platform for LBS, the smartphone revolution has redefined mobile application development. Advanced devices equipped with GPS, sensors, and augmented reality capabilities have paved the way for dedicated LBS applications that provide seamless and immersive user experiences.

3.3 Positioning Technologies

The paper’s recognition of accurate location determination remains pertinent. However, the advancement of positioning technologies, including Global Navigation Satellite Systems (GNSS), Wi-Fi-based positioning, and indoor positioning systems, has revolutionized location accuracy and enabled the development of hyper-localized LBS applications.

3.4 Data Privacy and Security

The paper briefly touched on privacy concerns. Subsequent to its publication, the landscape of data privacy and security has been fundamentally altered by regulations like GDPR. Modern LBS development places a heightened emphasis on protecting user data, warranting comprehensive discussions on privacy mechanisms and legal considerations.

4. Modern Considerations

4.1 Cloud Computing

An area that the original paper did not explore extensively is cloud computing. Cloud services have revolutionized LBS platforms, enabling scalable data storage, real-time data processing, and enhanced accessibility. The paper’s focus on SOAP-based web services could benefit from a broader discussion of cloud-based architectures.

4.2 Real-time Data and AI

Advancements in LBS extend to the integration of real-time data feeds and artificial intelligence (AI). AI-driven algorithms analyze location data to offer personalized recommendations, optimize routes, and predict user behavior. This dimension of LBS development has far-reaching implications for user engagement and satisfaction.

4.3 User Experience (UX)

Although the original paper mentioned user interface design, it did not delve into the critical aspect of user experience (UX). In the modern context, creating intuitive interfaces, employing responsive design principles, and prioritizing user-centric features are paramount for the success of LBS applications.

5. Conclusion

The original paper “The Development of Location Based Services in Malaysia” was instrumental in sparking discussions about LBS within the context of NSDI and OpenLS. However, the transformative technological advancements and shifting landscape of LBS since its publication necessitate a comprehensive reevaluation. While the original paper contributed historical insights into OpenLS and SOAP-based web services, a more encompassing analysis considering recent trends, standards, APIs, cloud computing, AI, privacy, and UX is essential to fully appreciate the evolution of LBS in contemporary contexts. As LBS continues to shape our digital world, understanding its journey is paramount for envisioning its future possibilities.

6. Reference

Ahmad Haris Abdul Halim, Sri Devi Ravana and Maizatul Akmar Ismail (2009). The Development of Location Based Services in Malaysia. [Online] Available at: https://www.geospatialworld.net/article/the-development-of-location-based-services-in-malaysia/ (Accessed:13 July 2023).

Suggestion for Citation:
Amerudin, S. (2023). The Evolution of Location Based Services: A Critical Review and Analysis. [Online] Available at: https://people.utm.my/shahabuddin/?p=6597 (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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A Review: Accuracy for the Masses: Real-Time Sub-Meter in a Consumer Receiver?

By Shahabuddin Amerudin

Accuracy for the Masses: Real-Time Sub-Meter in a Consumer Receiver?

Authors: Joshua Critchley-Marrows, Marco Fortunato and William Roberts

Publication Date: April 6, 2020

The article discusses a new methodology aimed at achieving sub-meter Global Navigation Satellite System (GNSS) accuracies in consumer devices such as smartphones. The goal is to enable applications like augmented reality and visually impaired navigation to function with higher precision. The article explores the challenges of achieving high accuracy in mass-market GNSS receivers, especially in the context of real-time positioning. It also introduces an alternative approach to Precise Point Positioning (PPP) to improve accuracy in challenging receiver environments.

Key Points:

  1. Challenges in Mass-Market GNSS Receivers: Mass-market GNSS receivers in consumer devices have limitations such as limited observation data from satellite constellations and restricted access to multi-frequency measurements. Additionally, the design of smartphones, with shared antennas and other communication hardware, can introduce noise and interference, affecting positioning accuracy. As a result, the typical accuracy achievable in ideal conditions is a few meters.
  2. Sub-Meter Accuracy Goals: The article sets a benchmark of achieving 50 cm accuracy for smartphone positioning due to the increasing demand for location-based apps requiring higher precision.
  3. The FLAMINGO Initiative: The methodology presented in the article is developed as part of the FLAMINGO initiative, which enables real-time PPP and Real-Time Kinematic (RTK) GNSS positioning on smartphones. The FLAMINGO service has achieved sub-meter positioning accuracies in real-time, but it relies on base station infrastructure within approximately 30 km of the user.
  4. Methodology Overview: The proposed methodology involves modifications to traditional GNSS processing. It includes a preprocessing stage to remove poor GNSS observables, a combination of single-frequency PPP with dual-frequency ionospheric-free combination, and signal-to-noise ratio and elevation-based noise weighting.
  5. Poor-Measurement Rejection: The preprocessing stage filters out poor GNSS observables using three detection strategies: Code-Minus-Carrier, Phase Range Rate, and Pseudorange Rate. These strategies identify and remove observations with errors caused by factors like multipath or cycle slips.
  6. SFDF Combination: To compensate for the mix of single-frequency and dual-frequency observations from smartphones or mass-market receivers, the methodology proposes the use of SFDF (Single Frequency, Dual Frequency) combination. This combination reduces error terms and improves vertical accuracy.
  7. Model Weighting: The methodology uses both elevation and signal-to-noise ratio (SNR) as weights in the GNSS processing algorithm. The combination of these weights enhances the precision of the positioning solution.
  8. Implementation and Test Results: The methodology is tested on real-time receiver trials, and the results are compared to an idealized post-processing scenario. The real-time solutions show less accuracy compared to the post-processed ideal case due to the challenges and limitations of smartphone environments. The SFDF model and SNR weighting show improvements in vertical accuracy.

Conclusion

The article presents a novel methodology to achieve sub-meter GNSS accuracies in consumer devices like smartphones. While the ideal case demonstrates high accuracy in post-processing, real-time implementation faces challenges due to the complex and noisy environment of smartphones. Nevertheless, the methodology shows promise for improving vertical accuracy and provides valuable insights for future GNSS research and development in consumer applications. Achieving sub-meter accurate PPP in real-time remains a goal with significant potential benefits.

Suggestion for Citation:
Amerudin, S. (2023). A Review: Accuracy for the Masses: Real-Time Sub-Meter in a Consumer Receiver? [Online] Available at: https://people.utm.my/shahabuddin/?p=6586 (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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Hybrid Positioning System

Combining GNSS (Global Navigation Satellite System) and indoor positioning technologies can be challenging since GNSS relies on signals from satellites that are often blocked by buildings and other obstacles in indoor environments. However, there are ways to combine these technologies to provide accurate positioning in both indoor and outdoor environments.

One approach is to use a hybrid positioning system that combines GNSS with other indoor positioning technologies, such as Wi-Fi, Bluetooth beacons, and inertial sensors. This system can provide accurate positioning in both indoor and outdoor environments by using GNSS when outdoors and switching to indoor positioning technologies when indoors.

The hybrid positioning system typically works as follows:

  1. GNSS positioning: When the device is outdoors, it uses GNSS positioning to determine its location. This involves receiving signals from satellites and using them to calculate the device’s position.

  2. Indoor positioning: When the device moves indoors, it switches to indoor positioning technologies like Wi-Fi, Bluetooth beacons, and inertial sensors. These technologies use signals from nearby sources to estimate the device’s position.

  3. Sensor fusion: The device uses sensor fusion techniques to combine the data from GNSS and indoor positioning technologies and create a more accurate and robust estimate of its location. Sensor fusion algorithms can account for the strengths and weaknesses of each technology and use that information to improve the overall positioning accuracy.

  4. Machine learning: The device may also use machine learning algorithms to analyze the sensor data and improve the accuracy of the positioning. Machine learning algorithms can learn from past data and adapt to changes in the environment over time, which can further improve the accuracy of the positioning.

By combining GNSS with indoor positioning technologies, the hybrid positioning system can provide accurate positioning in both indoor and outdoor environments. This approach can be useful in many applications, such as navigation, asset tracking, and location-based services.

However, it’s worth noting that combining GNSS with indoor positioning technologies can be challenging, and the accuracy and performance of the system can depend on a variety of factors, such as the density and distribution of indoor sensors, the environment, and the specific combination of technologies used.

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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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Terms – Geographic(al) Information System, GIScience, Geomatics, Geoinformatics, Geoinformation Technology and Geospatial Technology

Common people, often, get confused with the terms Geographic(al) Information System, GIScience, Geomatics, Geoinformatics, Geoinformation Technology and Geospatial Technology. To understand the differences or similarities among them we need to fine-tune our understanding about these frequently used and interchangeable terms.

Geographic Information System (GIS) is a computer-based information system used to digitally represent and analyze the geospatial data or geographic data. The GIS has been called an ‘enabling technology’, because it offers interrelation with the wide variety of disciplines which must deal with geospatial data. Each related field provides some of the techniques which make up a GIS. Many of these related fields emphasize data collection; GIS brings them together by emphasizing integration, modelling, and analysis. GIS has many alternative names used over the years with respect to the range of applications and emphasis; e.g., land information system, AM/FM–automated mapping and facilities management, environmental information system, resources information system, planning information system, spatial data-handling system, soil information system, and so on.

However, GIS may be considered as a type of software in a computer system that allows us to handle information about the location of features or phenomena on the earth’s surface, which has all the functionalities of a conventional DBMS plus much of the functionality of a computer mapping system. But software or an information system cannot be used in a vacuum. We need proper knowledge to develop it, to use it, and to make decisions from it. From this point of view, GIS is not just an advanced type of information systems, but a combination of science and technology, which has several interrelated distinct disciplines. Some of the interrelated important disciplines are geography, cartography, remote sensing, photogrammetry, surveying, geodesy, global navigation satellite system (GNSS), statistics, operations research, computer science, mathematics, and civil engineering.

As the integrating field, GIS often claims to be a science–Geospatial Information Science or Geographic Information Science. In the strictest sense, GIS is a computer system capable of integrating, storing, editing, analyzing, sharing, and displaying geographically referenced information. In a more generic sense, GIS is a tool that allows users to create interactive queries (user defined searches), analyze the geospatial information, and edit geospatial data. Geographical Information Science (often written as GIScience) is the science underlying the applications and systems. It is closely related to GIS but is not application-specific like GIS. For instance, analysis techniques, visualisation techniques, and algorithms/scientific logics for geographical data analysis are all part of GIScience.

GIScience is very much related with the term Geoinformatics that is a shorter name for Geographic Information Technology. Geographic information (also called geoinformation) is created by manipulating geographic (or geospatial) data in a computer system. Geoinformatics is a science and technology, which develops and uses information science infrastructure to address the problems of Geosciences (another name for Earth sciences) and related branches of engineering. Prakash (2006) defined Geoinformatics as “the collection, integration, management, analysis, and presentation of geospatial data, models and knowledge that support disciplinary, multidisciplinary, interdisciplinary and transdisciplinary research and education”. The four main tasks of Geoinformatics are: (1) collection and processing of geodata (geodata is the contraction of geographic data), (2) development and management of databases of geodata, (3) analysis and modelling of geodata, and (4) development and integration of logic and computer tools and software for the first three tasks. Geoinformatics uses GeoComputation (see note below) and it is the development and use of remote sensing, GIS, and GNSS. 

According to Virrantaus and Haggrén (2000) geoinformatics is a combination of remote sensing and GIS (they used the term Geoinformation Technique (GIT) instead of GIS technology). For example spatial analysis is a field in which image processing and GIS software tools are mixed and used together. It is very good experience to realize how same functionality can be achieved by using either image processing software tool or traditional GIS analysis tool within the embrace of Geoinformatics. 

Geoinformatics is not only for the people from surveying or geography but recently more and more people from other disciplines like Computer Science, Civil Engineering, Architecture, Geology etc. want to study Geoinformatics as their minor or even as their major subject (Virrantaus and Haggrén 2000). For that reason it has been most important to develop the contents of Geoinformatics curriculum towards more scientific subject and less being related with traditional surveying and mapping. People who wish to apply RS and GIS in their own problems among landscape design, geology or software development do not want to get profound knowledge on field measurements or printing technology. Geoinformatics as a mathematically and computationally oriented subject concentrates on data modeling and management, analysis and visualization processes and algorithms, GeoComputation, spatial statistics and operations research applications, development of GIS, image interpretation and satellite mapping technology (Virrantaus and Haggrén 2000).

Geoinformatics is a subset of Geomatics (also called Geomatics Engineering). In addition to topics within the confines of Geoinformatics, Geomatics emphasizes traditional surveying and mapping. The term ‘Geomatics’ relates both to science and technology, and integrates the following more specific disciplines and technologies: geodesy, traditional surveying, GNSS and their augmentations, cartography, remote sensing, photogrammetry, and GIS. An alternative view is that geomatics is the measurement and survey component of the broader field of GISscience. Geomatics is the discipline of gathering, storing, processing, and delivering of geoinformation or spatially referenced information.

The term Geomatics is fairly young, apparently being coined by B. Dubuisson in 1969. Originally used in Canada, because it is similar in French and English, the term geomatics has been adopted by the International Organization for Standardization, the Royal Institution of Chartered Surveyors, and many other international authorities, although some (especially in the United States) have shown a preference for the term ‘Geospatial Technology’.

Geomatics (or Geospatial Technology) is all about geospatial data. Although, precise definition of geomatics is still in flux; a good definition can be given from the University of Calgary’s web page: “Geomatics Engineering is a modern discipline, which integrates acquisition, modelling, analysis, and management of spatially referenced data, i.e. data identified according to their locations. Based on the scientific framework of geodesy, it uses terrestrial, marine, airborne, and satellite-based sensors to acquire spatial and other data. It includes the process of transforming spatially referenced data from different sources into common information systems with well-defined accuracy characteristics”. Konecny (2002) said “Geomatics, composed of the disciplines of geopositioning, mapping and the management of spatially oriented data by means of computers, has recently evolved as a new discipline from the integration of surveys and mapping (geodetic engineering) curricula, merged with the subjects of remote sensing and GIS”. Geopositioning refers to identifying the real-world geographic position by means of GNSS or any other surveying technique.

A number of University Departments which were once titled Surveying, Survey Engineering or Topographic Science, have re-titled themselves as Geomatics or Geomatics Engineering. According to Konecny (2002), geomatics has originated from surveying, mapping, and geodesy. Earlier, in higher education, the specialization was possible in one field such as geodesy or photogrammetry, but a comprehensive orientation toward surveying and mapping was lacking. Since about 1960 a technological revolution has taken place in surveying and mapping technology: angular surveys have been augmented by electronic distance measurement, and more recently by GNSS. Digital computers were able to statistically analyze huge measurement sets. Photogrammetry has become an analytical discipline, competing in accuracy with ground surveys. Earth observation by satellites has made remote sensing an indispensable tool. Cartography relying on tedious graphic work has made way to computer graphics. GIS has permitted to organize spatially oriented data in databases for the management of global, regional and local problems. The need for sustainable development has recently made obvious, that spatially referenced data constitute a needed infrastructure (spatial data infrastructure), to which all governments subscribe. Surveying and mapping curricula have traditionally provided the vision for the provision, updating, management and dissemination of spatially referenced data. However, there was a need to upgrade the curriculum orientation to modern tools and to society’s requirements. This is the reason why many programs have changed their name to ‘Geomatics’.

NOTE
GeoComputation is an emergent paradigm (class of elements with similarities) for multidisciplinary/interdisciplinary research that enables the exploration of previously insolvable, extraordinarily intricate problems in geographic context. Some people see GeoComputation as an incremental development rather than something entirely new. Several doubt that GeoComputation will make any real contribution to the sciences. Others view GeoComputation as a follow-on revolution to GIS. Openshaw (2000) argues GeoComputation is not just using computational techniques to solve spatial problems, but rather a completely new way of doing science in a geographical context.


References
Konecny, G. (2002). Recent global changes in geomatics education. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXIV, Part 6, pp. 9-14.
Openshaw, S. (2000). GeoComputation. In: S. Openshaw and R.J. Abrahart (eds.), GeoComputation, Taylor & Francis, New York, pp. 1-31.
Prakash, A. (2006). Introducing Geoinformatics for Earth System Science Education. Journal of Geoscience Education. URL: http://findarticles.com/p/articles/mi_q … _n17190422
University of Calgary’s web page: http://www.geomatics.ucalgary.ca/about/whatis
Virrantaus, K. and Haggrén, H. (2000). Curriculum of Geoinformatics — Integration of Remote Sensing and Geographical Information Technology. International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, Part B6, pp. 288-294.

Source: http://basudebbhatta.blogspot.com/2010/02/geographical-information-system.html

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Where My Location Data come from?

Basically, your Browser feature -Geolocation-, will try to determine your position using one of these several ways. These list, show the ordered devices about what Geolocation will give your location.

  1. GPS (Global Positioning System)
    This happen for smartphone / anything which has GPS inside. If you have smartphone with GPS capabilities and set to high accuracy mode, you’ll likely to obtain the location data from this. GPS calculate location information from GPS satelite signal. It has the highest accuracy. In most Android smartphone, the accuracy can be up to 10 metres.
  2. Mobile Network Location
    This happen if you use a wireless modem or phone without GPS chip built in it. Rather than GPS satellite’s signal, this one use signal from mobile provider. The accuracy may vary. 
  3. WiFi Positioning System
    If you are indoor, and using Wifi, this is the likely you’ll get. Some WiFi have location services capabilities, which able to obtain or save location data. If you’re concern with this stuff, try accessing this website from laptop with your Wifi network. If you can get exact location with very good accuracy, then your WiFi might have such feature. 
  4. IP Address Location
    This one will detects your location based on nearest Public IP Address on your devices, (can be your computer, or the router, or your ISP provider). Depend on the IP information available, but in many case, the public IP is often hidden behind Internet Service Provider NAT, resulting poor accuracy. This is the most often case for PC / laptop user which access internet from cable LAN, or WiFi without Geolocation capabilities. The accuracy is in level of city, region, or even country.

Source: https://mycurrentlocation.net