Kalman-Powered Tracking and Geofencing


Authors : Jovin P John; Abhijith A; Nadha K N; Denin Antony; Paul Ansel V

Volume/Issue : Volume 10 - 2025, Issue 8 - August


Google Scholar : https://tinyurl.com/2j2h8wfa

Scribd : https://tinyurl.com/yr6v2x6s

DOI : https://doi.org/10.38124/ijisrt/25aug123

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.


Abstract : Accurate real-time location tracking is critical for applications ranging from fleet management to personal safety. Conventional GPS systems, however, suffer from errors caused by signal multipath effects, atmospheric interference, and urban canyon distortions. This paper presents a low-cost, cloud- integrated GPS tracking system augmented with a Kalman filter and dynamic geofencing designed to mitigate these limitations. Leveraging an ESP32 microcontroller and a u-blox NEO-6M module, the proposed architecture achieves position updates at 1 Hz while filtering sensor noise using a computationally efficient Kalman implementation. Field trials demonstrate a 62% reduction in median absolute positional error compared to raw GPS data, consistently achieving sub-3-meter accuracy in urban environments. Processed coordinates are transmitted to a Firebase Realtime Database via Wi-Fi, enabling live visualization on a web interface with path-history mapping, where previous coordinates are displayed as a trail on the map, and user- defined geofencing. Circular geofences are dynamically monitored using the Haversine formula, triggering real-time alerts when boundary breaches occur. The system’s responsiveness is validated through latency measurements (<500 ms end-to- end delay), with energy consumption optimized to 85 mA during active tracking modes. By integrating low-cost hardware, adaptive filtering, and cloud analytics, this work provides a scalable solution for IoT applications such as logistics surveillance and emergency response systems, addressing gaps in both precision and affordability for real-time location- based services.

Keywords : Kalman Filter, GPS Tracking, Geofencing, ESP32, Firebase, Haversine Formula, Real-Time Monitoring, IoT, Location-Based Services.

References :

  1. E. D. Kaplan and C. Hegarty, *Understanding GPS: Principles and Applications*, 3rd ed. Norwood, MA, USA: Artech House, 2017.
  2. J. J. Ruiz et al., “Performance of GPS Positioning Protocols for Urban Navigation,” *IEEE Trans. Intell. Transp. Syst.*, vol. 23, no. 6, pp. 5525–5537, Jun. 2022, doi: 10.1109/TITS.2021.3069367.
  3. M. S. Grewal and A. P. Andrews, *Kalman Filtering: Theory and Practice with MATLAB*, 5th ed. Hoboken, NJ, USA: Wiley, 2020.
  4. Google, *Firebase Realtime Database*, 2023. [Online]. Available: https://firebase.google.com/ docs/database
  5. C. Veness, *Calculate Distance and Bearing Using Haversine Formula*, 2023. [Online]. Available: https://www.movable- type.co.uk/scripts/latlong.html
  6. R. Pratama et al., “IoT-Based Vehicle Tracking Using GSM Modules,” *IEEE Sens. J.*, vol. 21, no. 4, pp. 5384–5392, Feb. 2021, doi: 10.1109/JSEN.2020.3033367.
  7. L. M. González et al., “Wildlife Tracking Using IoT Devices,” *IEEE Internet Things J.*, vol. 10, no. 8, pp. 6794–6805, Apr. 2023, doi: 10.1109/JIOT.2023.3245678.
  8. F. Almarzouki et al., “Real-Time Asset Tracking Using Cloud Platforms,” *IEEE Access*, vol. 11, pp. 12345–12358, 2023, doi: 10.1109/ACCESS.2023.3245678.
  9. Espressif Systems, *ESP32 Technical Reference Manual*, 2023. [Online]. Available: https://www.espressif.com/en/products/socs/esp32/resources
  10. u-blox, *NEO-6M GPS Module Datasheet*, 2023. [Online]. Available: https://www.u-blox.com/en/product/neo-6m

Accurate real-time location tracking is critical for applications ranging from fleet management to personal safety. Conventional GPS systems, however, suffer from errors caused by signal multipath effects, atmospheric interference, and urban canyon distortions. This paper presents a low-cost, cloud- integrated GPS tracking system augmented with a Kalman filter and dynamic geofencing designed to mitigate these limitations. Leveraging an ESP32 microcontroller and a u-blox NEO-6M module, the proposed architecture achieves position updates at 1 Hz while filtering sensor noise using a computationally efficient Kalman implementation. Field trials demonstrate a 62% reduction in median absolute positional error compared to raw GPS data, consistently achieving sub-3-meter accuracy in urban environments. Processed coordinates are transmitted to a Firebase Realtime Database via Wi-Fi, enabling live visualization on a web interface with path-history mapping, where previous coordinates are displayed as a trail on the map, and user- defined geofencing. Circular geofences are dynamically monitored using the Haversine formula, triggering real-time alerts when boundary breaches occur. The system’s responsiveness is validated through latency measurements (<500 ms end-to- end delay), with energy consumption optimized to 85 mA during active tracking modes. By integrating low-cost hardware, adaptive filtering, and cloud analytics, this work provides a scalable solution for IoT applications such as logistics surveillance and emergency response systems, addressing gaps in both precision and affordability for real-time location- based services.

Keywords : Kalman Filter, GPS Tracking, Geofencing, ESP32, Firebase, Haversine Formula, Real-Time Monitoring, IoT, Location-Based Services.

CALL FOR PAPERS


Paper Submission Last Date
30 - November - 2025

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe