Abstract: To reduce the negative effect in the WiFi fingerprint localization algorithm caused by the fluctuation of the received signal strength (RSS)， a WiFi fingerprint localization algorithm based on sliding window combined with the longest common subsequence was proposed. First， the time sequence RSS fingerprints were converted to the sliding window data model to increase the fingerprint characteristic information and improve the matching accuracy. And then， the requesting location data and the longest common subsequence were calculated to get the similarity of sampling points， which could solve the problem caused by the window scaling or the individual sampling point without signal in the sliding window， thereby the localization accuracy and robustness were improved. The results showed that the proposed localization algorithm was superior to the instantaneous fingerprints localization algorithm.

Key words: indoor localization, fingerprint, sliding window, time series, longest common subsequence