<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

Data publikacji: 01 cze 2016

Zakres stron: 1054 - 1072

Otrzymano: 22 lut 2016

Przyjęty: 11 kwi 2016

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

Słowa kluczowe
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

Data publikacji: 01 cze 2016

Zakres stron: 1054 - 1072

Otrzymano: 22 lut 2016

Przyjęty: 11 kwi 2016

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

Słowa kluczoweLocalization, 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.

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