Optimal KNN Positioning Algorithm via Theoretical Accuracy Criterion in WLAN Indoor Environment

This paper proposes the optimal K nearest neighbors (KNN) positioning algorithm via theoretical accuracy criterion (TAC) in wireless LAN (WLAN) indoor environment. As far as we know, although the KNN algorithm is widely utilized as one of the typical distance dependent positioning algorithms, the optimal selection of neighboring reference points (RPs) involved in KNN has not been significantly analyzed. Therefore, in order to fill this gap, the optimal KNN positioning algorithm based on the best TAC is introduced. And this algorithm is beneficial to construct the reliable WLAN indoor positioning system and provide the efficient location based services (LBSs). The relationship among theoretical expectation accuracy, unit interval of neighboring RPs and dimensions of target location region is also revealed. Furthermore, the feasibility and effectiveness of optimal KNN positioning algorithm are verified based on the experimental comparisons respectively in the regular office room, straight corridors, static positioning and dynamic tracking situations.