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A WiFi Indoor Positioning Strategy Based on Two-step Fingerprint Matching

In the WiFi-based positioning system, the quality of received signal strength fingerprint samples directly affects positioning accuracy. This paper proposes a new database optimization and positioning matching method. In the offline phase, an optimal fingerprint database is formed by removing the outliers using threshold-based data pre-processing method; a high-density virtual fingerprint database based on above is generated by Gaussian process regression model. In the online phase, the outliers in fingerprint sample obtained in real-time are removed in the same way. Then, access point weighted multiple matching nearest neighbor algorithm based on Pearson correlation coefficient is used to initially determine the target region with optimized fingerprint database; the improved weighted k-nearest neighbor algorithm is utilized to calculate the target location with high-density virtual fingerprint database. Experimental results show that, compared with other algorithms, our proposed method can significantly improve the positioning accuracy while ensuring real-time performance.