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

TLDR

Indoor tracking and localization are increasingly important, yet GPS accuracy is often insufficient indoors. The study investigates a cost‑effective, large‑scale indoor tracking system that operates over existing Wi‑Fi networks and can incorporate new sensor observations. A sigma‑point Kalman smoother fuses a human walking dynamic model with low‑cost sensors—Wi‑Fi RSSI from an Ekahau receiver tag, binary IR motion sensors, and foot‑switches—to estimate 2‑D position and velocity, and its performance is benchmarked against Ekahau’s commercial engine. The proposed algorithm achieves superior accuracy compared to the commercial engine across multiple trials.

Abstract

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.