SAP: A Novel Stationary Peers Assisted Indoor Positioning System

Recent years have witnessed significant advances in indoor positioning. Existing approaches either require cumbersome site surveys or customized hardware, thus hindering their popularity. In this paper, we propose Stationaly peers-Assisted indoor Positioning (SAP), a practical indoor positioning solution that is highly scalable and easy-to-deploy. SAP greatly alleviates the pain of fingerprint collection by smartly leveraging the relatively stationary people, namely stationary peers that are largely available in common indoor environments to assist positioning. Once SPs' locations and the relative distance between SPs and a target are obtained, SAP can apply trilateration to locate the target. SAP incorporates three key modules: a novel accelerometer-based filter that can accurately identify SP, an enhanced fingerprint-based positioning method that can accurately pinpoint SPs' locations, and a robust acoustic ranging method. We implement a prototype of SAP on the Android platform and evaluate its performance in representative real-world environments. SAP achieves an 80% positioning error of 2.2 m, which is comparable to the most existing smartphone-assisted approaches.