Abstract:The received signal strength indication (RSSI) based indoor fingerprinting positioning algorithm has problems of referencepoints errormatching and location discovery. To solve these problems, a fuzzy clustering and regional cat swarm based positioning method is proposed. Firstly, the fuzzy clustering is used to accomplish clustering and estimate RSSI feature of the cluster center instead of the traditional hard clustering algorithm. In this way, the fuzzy clustering based twolevel matching can increase the difference between reference points, and reduce the complexity of feature matching. Then, the cat swarm optimization is utilized due to the fast convergence near the optimal solution, which is suitable for the location discovery based on the regions obtained by the twolevel matching method. Simultaneously, a feed mechanism is designed to improve the local search capability and the convergence speed of the cat swarm optimization. Compared with traditional algorithms, experimental results show that the proposed algorithm can improve the positioning accuracy by 12.5%.