Implementation of a Kalman Filter to Improve RSSI-Based Trilateration Accuracy Using the LoRa RFM95W Module

Abstract

Internet of Things (IoT)-based positioning has become increasingly important for monitoring and navigation applications. This study proposes a LoRa-based positioning system using Received Signal Strength Indicator (RSSI) measurements filtered by a Kalman Filter. The filtered RSSI values are converted into distance using the Log-Distance Path Loss model, while the target position is estimated through trilateration. Positioning accuracy is evaluated by comparing the estimated coordinates with GPS coordinates obtained from a u-blox NEO-6M module using the Haversine distance. Experimental results show that the Kalman Filter improves RSSI stability and reduces distance fluctuations, leading to more consistent position estimates. However, the positioning accuracy remains lower than GPS, particularly in dynamic environments. Despite this limitation, the proposed system demonstrates the potential of LoRa as a low-power, long-range positioning solution. Future work will focus on adaptive filtering methods to further improve positioning accuracy.