Paper Info
Title
Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking
Abstract
Received signal strength (RSS)-based device-free localization applications utilize the communication between wireless devices for locating people within the monitored area. The technology is based on the fact that humans cause changes in properties of the wireless channel which is observed in the RSS, enabling localization of people without requiring them to carry any sensor, tag or device. Typically this inverse problem is solved using an empirical model that relates the RSS to location of the sensors and person, and utilizing either an imaging method or a particle filter (PF) for positioning. In this paper, we present an extended Kalman filtering (EKF) solution that incorporates some of the beneficial properties of the PF but has a lower computational overhead. In order to make the EKF work, we also need to reconsider how the measurements are sampled and processed, and a new processing scheme is proposed. The developments are validated using simulations and experimental data, and the results imply: i) the non-linear filters outperform a popular imaging method; ii) the robustness of the EKF and PF is improved using the proposed processing scheme; and iii) the EKF achieves similar performance as the PF as long as the new processing scheme is used.
Year
DOI
Venue
2018
10.1109/IPIN.2018.8533772
2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Keywords
Field
DocType
received signal strength,wireless sensor networks,Bayesian filtering,positioning and tracking
Overhead (computing),Computer vision,Extended Kalman filter,Wireless,Particle filter,Algorithm,Kalman filter,Robustness (computer science),Artificial intelligence,Engineering,Wireless sensor network,RSS
Conference
ISSN
ISBN
Citations 
2162-7347
978-1-5386-5636-5
0
PageRank 
References 
Authors
0.34
14
4
Name
Order
Citations
PageRank
Ossi Kaltiokallio122114.41
Roland Hostettler2216.53
Neal Patwari33805241.58
Riku Jäntti477392.13