<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper addresses the problem of mobile sensor localization and tracking in an obstructed environment. To solve this problem, a combination of three approaches is proposed: a nonlinear Kalman Filter to estimate the mobile position, a sub filter used jointly with the nonlinear filter to estimate the bias due to the Non-Line Of Sight (NLOS) effect and a low complexity method for Line Of Sight (LOS) and NLOS identification. Based on hypothesis testing, this method discriminates between the LOS and NLOS situations using a sequence of estimated biases. Simulation results show that the proposed method provides good positioning accuracy</p>
</abstract>

This paper addresses the problem of mobile sensor localization and tracking in an obstructed environment. To solve this problem, a combination of three approaches is proposed: a nonlinear Kalman Filter to estimate the mobile position, a sub filter used jointly with the nonlinear filter to estimate the bias due to the Non-Line Of Sight (NLOS) effect and a low complexity method for Line Of Sight (LOS) and NLOS identification. Based on hypothesis testing, this method discriminates between the LOS and NLOS situations using a sequence of estimated biases. Simulation results show that the proposed method provides good positioning accuracy

Mobile Localization And Tracking With Los And Nlos Identification In Wireless Sensor Networks

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper addresses the problem of mobile sensor localization and tracking in an obstructed environment. To solve this problem, a combination of three...

Mobile Localization And Tracking With Los And Nlos Identification In Wireless Sensor Networks

Published Online: Jun 01, 2016

Page range: 1054 - 1072

Received: Feb 22, 2016

Accepted: Apr 11, 2016

DOI: https://doi.org/10.21307/ijssis-2017-907

Keywords
Localization, Tracking, Wireless Sensor Network, Non Line of Sight, Divided Difference Kalman Filter, Unscented Kalman Filter, Bias estimation, Hypothesis testing

© 2016 Y. K. Benkouider et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mobile Localization And Tracking With Los And Nlos Identification In Wireless Sensor Networks

Published Online: Jun 01, 2016

Page range: 1054 - 1072

Received: Feb 22, 2016

Accepted: Apr 11, 2016

DOI: https://doi.org/10.21307/ijssis-2017-907

KeywordsLocalization, Tracking, Wireless Sensor Network, Non Line of Sight, Divided Difference Kalman Filter, Unscented Kalman Filter, Bias estimation, Hypothesis testing

© 2016 Y. K. Benkouider et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Localization, Tracking, Wireless Sensor Network, Non Line of Sight, Divided Difference Kalman Filter, Unscented Kalman Filter, Bias estimation, Hypothesis testing