Abstract:

Considering the problem that it is difficult for the traditional clustering method to divide physical space effectively,and that the positioning error is large due to instability of the signal source,aiming at reducing the storage cost of database and improving the quality of the fingerprint,this paper proposes a simplified access point matching location algorithm based on fuzzy clustering.According to the proposed algorithm,in the offline stage,the target space of a large area is divided into multiple overlapping fuzzy partitions by the characteristics of the signal source,the stability,visibility,redundancy and other multi-scale characteristics of the signal source in each partition are comprehensively considered,the smallest access point identification set in the area is established,the positioning speed is improved and the defects of mismatch caused by the unstable access point are decreased.In the position calculation stage,the traditional Euclidean distance is improved by assigning the weight of the neighbor points in combination with the stability characteristics of the regional access points,and the speed constraint relationship between adjacent moments during the movement of the user to be located is used to filter the positioning outliers,to overcome the changes in the environment and signal sources.The unfavorable influence of the locating error is reduced.Tested on actual scenes,the proposed algorithm reduces the computational consumption of the positioning algorithm on the premise of effectively screening the access points,while significantly reducing the offline data storage and controlling the average positioning error of the positioning scene within 1m.Compared with the existing classic positioning methods,the positioning accuracy of this algorithm is improved by more than 15%.

Key words: indoor positioning, fingerprint positioning, fuzzy clustering, access point selection