Accuracy-Enhanced Position-Sensing via Kalman Filter Based on Received Signal Strength

With the rapid development of today's Internet of Things technology, the integration of communication and sensing is becoming more and more important. Sensing position by the received signal strength (RSS) is a widely used technique. Its low cost and low difficulty in obtaining signals make it one of the most effective indoor sensing technologies. However, due to the inevitable issues of shadowing and path loss fadings, it remains challenging to achieve high-accuracy positioning with RSS. Therefore, this paper proposes a positioning solution based on RSS that uses Kalman filtering to improve accuracy. More precise positioning is achieved by filtering the received signal and using the data to derive a more accurate path-loss fading exponent. According to the simulation of numerical data, it is verified that this method can achieve enhanced accuracy in positioning with RSS under strongly dynamic shadowing conditions.