Wi-Fi based indoor localization and tracking using sigma-point Kalman filtering methods

Estimating the location of people and tracking them in an indoor environment poses a fundamental challenge in ubiquitous computing. The accuracy of explicit positioning sensors such as GPS is often limited for indoor environments. In this study, 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. At the core of our system is a novel location and tracking algorithm using a sigma-point Kalman smoother (SPKS) based Bayesian inference approach. The proposed SPKS fuses a predictive model of human walking with a number of low-cost sensors to track 2D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infrared (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.