RSSI-Based Indoor Positioning System on ESP32 with Kalman Filtering and Trilateration

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.