Enhanced WiFi ToF indoor positioning system with MEMS-based INS and pedometric information

Abstract

The most common technology for outdoor positioning is GNSS. It is commonly used together with inertial sensors to compensate for poor reception and to help determine outlier measurements. In dense areas and indoors, GPS performance degrades or is not available at all. In indoor environments WiFi is one of the most popular radios; it is not surprising therefore that WiFi is often used for positioning. Specifically, time-based range measurements are emerging as the leading WiFi indoor positioning technology. Because this technique is quite new, its coverage might be limited in the near future. In this paper we present a highly accurate indoor positioning system which is based on a new WiFi technology (protocol) [1] and on MEMS inertial sensors. This system fuses together WiFi time-of-flight (ToF) range measurements, INS-based position velocity and attitude measurements, and pedometric information. It harnesses the advantages of each of these components while compensating for their individual disadvantages. WiFi ToF typically exhibits good performance but suffers from outliers, coverage and dependency of Access Points (AP) deployment geometry (DoP). The INS solution is highly accurate but diverges quickly with time. Pedometric information (PDR) suffers from overall poor performance, inability to determine direction of movement (heading) and exhausting per-user calibration. Our solution uses WiFi ToF measurements and pedometric information to restrict the INS solution. We describe the INS model, the fusion model, and show exciting results from a real world environment.