RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers

Solutions for indoor tracking and localization have become more critical with recent advancement in context and location-aware technologies. The accuracy of explicit positioning sensors such as global positioning system (GPS) is often limited for indoor environments. In this paper, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. This paper proposes a sigma-point Kalman smoother (SPKS)-based location and tracking algorithm as a superior alternative for indoor positioning. The proposed SPKS fuses a dynamic model of human walking with a number of low-cost sensor observations to track 2-D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infra-red (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau, Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau, Inc. The superior accuracy of our approach over a number of trials is demonstrated.

I. Introduction and Related Work

Location and context-aware technologies play a critical role in emerging next generation mobile applications. Example goals of these applications range from tracking assets within large warehouses, monitoring people inside assisted living communities, to adapting user interfaces based on location and activity. Key to each application is the ability to accurately localize and track an individual or asset. Explicit positioning sensors based on Global Positioning System (GPS) work worldwide and can sometimes achieve centimeter accuracy. However, GPS generally requires a direct view to several satellites, resulting in limited performance for indoor environments. Development of non-GPS-based solutions are thus of great interest for indoor use based on both existing signals and hardware, as well as new systems and sensor modalities. Additional design constraints pose significant challenges for development of such systems, including calibration overhead, user privacy, and the high variability of wireless channels.