Indoor Multifloor Localization Method Based on WiFi Fingerprints and LDA

Abstract

Indoor localization has elicited increasing attention because it has been widely used in indoor location-based services. At present, many complex scenarios for indoor localization require position estimation not only in single-floor environments but also in multifloor ones. However, existing works exhibit certain limitations in solving the problems that involve high computational complexity and floor localization accuracy. In this paper, a multifloor identification system based on WiFi fingerprint database is designed to address these issues. This floor identification system is divided into offline and online phases. In the offline phase, a localization fingerprint database is built based on WiFi nodes and a multifloor identification model is proposed based on linear discriminant analysis (LDA), called MA_LDA. In the online phase, the final floor number is determined, and the trained model is combined with the majority voting mechanism. After determining the floor number, an algorithm based on the k-nearest neighbor (KNN), called LL_KNN, is proposed to obtain the location information of a target on the floor. Real experiment results show that our system can identify the floor number by using only a little WiFi node fingerprint information rather than all the nodes to reduce the computational complexity. It works efficiently and achieves high fault-tolerance performance compared with existing approaches in locating targets in a multifloor environment.