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

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 calibrationfree 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.