Abstract
There are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward an adaptive weighted K-nearest neighbor fingerprint positioning method considering the user’s orientation. First, the orientation fingerprint database model is proposed, which includes the position, orientation, and the sequence of mean received signal strength indicator at each reference point. Second, the fuzzy c-means algorithm is used to cluster orientation fingerprint database taking the hybrid distance of the signal domain and position domain as the clustering feature. Finally, the proposed adaptive algorithm is developed to select K-reference points by matching operation, to remove the reference points with larger signal-domain distances, minimum and maximum coordinate values, and calculate the weighted mean coordinates of the remaining reference points for positioning results. The experimental results show that the average error decreases by 0.7 m, and the root mean square error decreases to about 1.3 m by the proposed technique. And, we conclude that the proposed adaptive weighted K-nearest neighbor fingerprint positioning method can improve positioning accuracy.
Highlights
The indoor positioning technology is a research focused on navigation and location-based services (LBS) and has attracted extensive attentions of research institutions, universities, and enterprises
ABI Research estimates that the commercial value of indoor locationbased services (ILBS) will be worth up to 10 billion dollars by the end of 2020.1 Nowadays, there are a large number of applications for ILBS in market
The traditional Wi-Fi fingerprint positioning method uses a common fingerprint database (CFPD) model, which takes the vector of mean received signal strength indicator (RSSI) values in multiple directions as signal features and ignores the deviations of RSSI values caused by the user’s orientation
Summary
The indoor positioning technology is a research focused on navigation and location-based services (LBS) and has attracted extensive attentions of research institutions, universities, and enterprises. The FCM algorithm is adopted to cluster for OFPD based on the hybrid distance, which is the fusion of signal-domain and position-domain distances; in the online stage, when the user requests localization, the OFPD is selected based on the user’s orientation and used for further matching operation. The traditional Wi-Fi fingerprint positioning method uses a common fingerprint database (CFPD) model, which takes the vector of mean RSSI values in multiple directions as signal features and ignores the deviations of RSSI values caused by the user’s orientation. The signal-domain distances between the test point (TP) and all clustering centers of the O1FPD are calculated, The clustering results based on the signal-domain distance could not reflect the exact spatial relationship of RPs. the hybrid distance, the fusion of signal-domain and position-domain distances, should be taken as the clustering feature in clustering analysis. 30: 31: 32: eNjxnrjesd==Nfousrmqr(tCKN),re1jÀx1jaiP v=Ngre=1 jNx1rðeiiÞP=NjreÀ1 jxjxijj,avg
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