An Effective Fingerprint-Based Indoor Positioning Algorithm Based on Extreme Values

Abstract

Wi-Fi-based fingerprint indoor positioning technology has gained special attention, but the development of this technology has been full of challenges such as positioning time cost and positioning accuracy. Therefore, selecting reasonable Wireless Access Points (APs) for positioning is essential, as the more APs used for positioning, the higher the online computation, energy and time cost. Furthermore, the received signal strength (RSS) is easily affected by diverse interference (obstacles, multipath effects, etc.), decreasing the positioning accuracy. AP selection and positioning algorithms are proposed in this paper to solve these issues. The proposed AP selection algorithm fuses RSS distribution and interval overlap degree to select a small number of APs with high importance for positioning. The proposed positioning algorithm uses the location distance between reference points (RPs) to construct a circle and leverages extreme values (maximum and minimum values) of circles to determine the possibility that the test point (TP) appears in each circle, then it finds useful APs to determine the weight of RPs. Extensive experiments are conducted in two different areas, and the results show the effectiveness of the proposed algorithm.

Keywords

• fingerprint positioning
• indoor positioning
• interval overlap degree
• extreme values
• Gaussian Process Regression