Research on Indoor Wi-Fi Positioning Method Based on Trilateral Positioning and Fingerprint Matching

With the growth of indoor positioning requirements, positioning technology based on Wi-Fi signals has become a research hotspot due to its wide deployment and low cost. Aiming at the large positioning accuracy error, this paper proposes an indoor positioning algorithm based on multimodal data fusion. The algorithm studies and analyzes key technologies such as path loss model, Kalman filter parameter optimization, and hybrid positioning weight allocation. By fusing trilateral positioning of Wi-Fi signal strength (RSSI) and fingerprint matching technology, combined with Kalman filter and weighted least square method optimization, the positioning accuracy is significantly improved. Experimental results show that in a typical indoor environment, the average positioning error of the method in this paper is reduced to within 1.5 m at standard measurement points. Compared with the existing method (trilateral positioning method without weighted calculation), the positioning accuracy is effectively improved.