| Summary: | K-means fingerprint localization can reduce the complexity of localization, and improving the real-time of location has become a hot-spot of current localization algorithm. However, the randomness of clustering has resulted in great instability to the localization. In this paper, two-step clustering algorithm is proposed to optimize the clustering number according to the AIC criterion. Considering the nearest neighbor algorithm would result in great error, correlation coefficient method is used to determine the highest similarity of the sub-library, and estimate the final position. The experimental results show that the optimized algorithm improves not only the positioning accuracy, but also the real-time and stability of localization.
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