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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • University of Utah
  • Xandem Technology LLC


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.


Original languageEnglish
Title of host publicationIPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation
Publication statusPublished - 13 Nov 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Indoor Positioning and Indoor Navigation - Nantes, France
Duration: 24 Sep 201827 Sep 2018
Conference number: 9

Publication series

NameInternational Conference on Indoor Positioning and Indoor Navigation
ISSN (Print)2162-7347
ISSN (Electronic)2471-917X


ConferenceInternational Conference on Indoor Positioning and Indoor Navigation
Abbreviated titleIPIN

    Research areas

  • Bayesian filtering, positioning and tracking, received signal strength, wireless sensor networks

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