Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2018-11-13
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation, International Conference on Indoor Positioning and Indoor Navigation
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.
Description
Keywords
Bayesian filtering, positioning and tracking, received signal strength, wireless sensor networks
Other note
Citation
Kaltiokallio , O , Hostettler , R , Patwari , N & Jäntti , R 2018 , Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking . in IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation . , 8533772 , International Conference on Indoor Positioning and Indoor Navigation , IEEE , United States , International Conference on Indoor Positioning and Indoor Navigation , Nantes , France , 24/09/2018 . https://doi.org/10.1109/IPIN.2018.8533772