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

Abstract

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 EkahauInc. 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.

An Improved Algorithm for Indoor Location

“…Trilateration, the angle threshold condition, weighted centroid location, differential location, weighted least squares method and other methods will be adopted to improve the location accuracy. The proposed indoor wireless location method is based on Received Signal Strength Indicator, RSSI, which calculates distances by received signal strength [9]. Currently, the most popular location method is based on RSSI [9].…”

“…The proposed indoor wireless location method is based on Received Signal Strength Indicator, RSSI, which calculates distances by received signal strength [9]. Currently, the most popular location method is based on RSSI [9].…”

Multi-group localization problem of service robots based on hybrid external localization algorithm with application to shopping mall environment

“…Therefore, localization is a fundamental ability to the deployment of the vast majority autonomous robot, and has been a continuous research subject for a long time [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. As technology evolves, sensors used to realize robot localization have been updated as well.…”

An Improved Indoor Localization of WiFiBased on Support Vector Machines

“…However, GPS is not reliable in densely-populated urban areas and almost unavailable in indoor areas. Many indoor localization algorithms have been proposed [6][7][8][9], and there has long been interested in the ability to determine the localization of the devices given only WiFi signal strength. In many proposed indoor localization algorithms, the signal strength measurements from different access points (APs) in a wireless local area network (WLAN) are used to infer the localization of indoor mobile devices [10][11][12][13][14].…”