AWKNN Indoor Location Methods Based on Kalman Smoothing

Aiming at the problem of the information jump in WIFI indoor location based on the received signal strength indication (RSSI), which influences the positioning accuracy, an improved adaptive weighted K nearest neighbor (AWKNN) localization method based on Kalman filter is proposed. In this paper, the feasibility of smoothing the RSSI algorithm is compared and analyzed, and the advantages of smoothing the RSSI based on the Kalman filter are verified. Combining with the AWKNN algorithm and taking advantage of the mean square deviation to calculate the matching degree, the size of denominator m in the mean square error can be adjusted automatically through monitoring the number of matching wireless access points in real time to achieve the effective control of positioning error. The experimental results show that the AWKNN algorithm based on Kalman filter is more effective than the traditional WIFI fingerprint algorithm in terms of stability and positioning accuracy.