Indoor localization algorithm based on combination of Kalman filter and clustering

摘要

Aiming at the problem of low accuracy and efficiency of traditional indoor positioning algorithm, a positioning algorithm is put forward, which combines with Kalman filter algorithm and clustering algorithm. This algorithm firstly divided the original fingerprint database into K clusters with k-means clustering algorithm and Gaussian mixture model algorithm, and then use the Kalman filtering algorithm to process the collected testing signal. The experimental results show that compared with the traditional indoor positioning algorithm, the joint positioning method reduces the signal noise, improves the efficiency and stability of the positioning algorithm, reduces the positioning error by 23%, and this algorithm works well in positioning.