RSSI-Based Indoor Positioning System on ESP32 with Kalman Filtering and Trilateration
Description
This work presents an RSSI-based indoor positioning system implemented using ESP32 microcontrollers and Wi-Fi signal strength measurements. The proposed system estimates the location of a target node using trilateration from multiple anchor nodes.
To improve localization accuracy in indoor environments, a Kalman Filter is applied to reduce RSSI fluctuations and measurement noise. The system combines wireless communication, signal processing, and mathematical localization techniques to provide low-cost indoor positioning suitable for smart buildings, IoT applications, and robotics.
The implementation includes:
• ESP32-based anchor and target nodes
• RSSI acquisition and filtering
• Distance estimation using path loss models
• Trilateration-based coordinate estimation
• Real-time localization visualization
Experimental results demonstrate improved stability and positioning accuracy after Kalman filtering compared to raw RSSI measurements.
Keywords: ESP32, Indoor Positioning System, RSSI Localization, Kalman Filter, Trilateration, Wireless Sensor Networks, IoT, Localization.
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Additional details
Dates
- Submitted
-
2026-05-20
Software
- Repository URL
- https://github.com/harshsaxena213/Indoor-Positioning-System-Application-On-ESP32
- Programming language
- Python
- Development Status
- Active
References
- M. R. Bshara, U. Orguner, F. Gustafsson, and L. Van Biesen, "Fingerprinting localization in wireless networks based on received-signal-strength measurements: A case study on WiMAX networks," IEEE Transactions on Vehicular Technology, 2011.
- P. Bahl and V. N. Padmanabhan, "RADAR: An in-building RF-based user location and tracking system," IEEE INFOCOM, 2000.
- R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," Journal of Basic Engineering, 1960.