Abstract: In traditional wireless fidelity(WiFi)fingerprint matching algorithms,factors such as remote proximity points caused by signal fluctuation and the occlusion of access point(AP)signals by objects in the environment will seriously affect the positioning accuracy.To solve this problem,this paper proposes an adaptive fingerprint subspace matching positioning algorithm.According to the combination of different APs,the fingerprint database and the test fingerprint are divided into subspaces.In each subspace,the difference between Euclidean distances is used to set the optimal critical value of performance,and the nearest K reference points are selected.The weighted K-nearest neighbor method is used for coarse positioning to eliminate the error caused by remote neighboring points.Finally,the estimated value of coarse position in each subspace is integrated,and the average filter is used for precise positioning.Experimental results show that,compared with the traditional WiFi fingerprint matching algorithm,the proposed algorithm effectively reduces the impact of remote proximity points and AP occlusion on the positioning accuracy,enhances the constraint of AP on different positions,and improves the accuracy and robustness of the WiFi positioning system.

Key words: Signal intensity, Subspace, Critical value, Self-adaption, Fingerprint matching