A Robust Wi-Fi Fingerprinting Indoor Localization Coping with Access Point Movement

Abstract

A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point (AP). However, typical fingerprinting localization methods fail to resist accidental environmental changes, such as AP movement. In order to address this problem, a robust fingerprinting indoor localization method is initiated. In the offline phase, three attributes of Received Signal Strength Indication (RSSI)—average, standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way, the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP's movement. In the following localization step, in order to achieve accuracy and efficiency simultaneously, a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance, thus realizing the optimization of MAximum Overlapping algorithm (MAO). Finally, experimental results are displayed, which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.