Projects per year
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.
Original language | English |
---|---|
Title of host publication | IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation |
Place of Publication | United States |
Publisher | IEEE |
ISBN (Electronic) | 9781538656358 |
DOIs | |
Publication status | Published - 13 Nov 2018 |
MoE publication type | A4 Conference publication |
Event | International Conference on Indoor Positioning and Indoor Navigation - Nantes, France Duration: 24 Sept 2018 → 27 Sept 2018 Conference number: 9 |
Publication series
Name | International Conference on Indoor Positioning and Indoor Navigation |
---|---|
ISSN (Print) | 2162-7347 |
ISSN (Electronic) | 2471-917X |
Conference
Conference | International Conference on Indoor Positioning and Indoor Navigation |
---|---|
Abbreviated title | IPIN |
Country/Territory | France |
City | Nantes |
Period | 24/09/2018 → 27/09/2018 |
Keywords
- Bayesian filtering
- positioning and tracking
- received signal strength
- wireless sensor networks
Fingerprint
Dive into the research topics of 'Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking'. Together they form a unique fingerprint.Projects
- 2 Finished
-
RFI: Narrow-band RF Inference (RFI) - Kapeakaistainen RF Inferenssi (RFI)
Jäntti, R. (Principal investigator), Ali, Y. (Project Member), Fellan, A. (Project Member) & Kaltiokallio, O. (Project Member)
01/09/2016 → 31/08/2020
Project: Academy of Finland: Other research funding
-
Crowdsourced mapping of the environment- multimodal real-time SLAM via combinedinertial, optical, and magnetic sensoring
Hostettler, R. (Project Member), Särkkä, S. (Principal investigator), Tronarp, F. (Project Member), Garcia Fernandez, A. (Project Member), Sarmavuori, J. (Project Member), Karvonen, T. (Project Member) & Raitoharju, M. (Project Member)
01/01/2016 → 31/12/2017
Project: Academy of Finland: Other research funding