WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation

Abstract

WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.