Fusing Fixed and Hint Landmarks on Crowd Paths for Automatically Constructing Wi-Fi Fingerprint Database

In typical Wi-Fi based indoor positioning systems employing fingerprint model, plentiful fingerprints need to be trained by trained experts or technician, which extends labor costs and restricts their promotion. In this paper, a novel approach based on crowd paths to solve this problem is presented, which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers. However, the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped. Therefore, we define two concepts： fixed landmark and hint landmark, to rectify the fingerprint database in the practical system, in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark. Machine- learning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space. Besides, the particle filter algorithm is also introduced to smooth the sample points in crowd paths. We implemented the approach on off-the-shelf smartphones and evaluate the performance. Experimental results indicate that the approach can availably construct Wi-Fi fingerprint database without reduce the localization accuracy.