Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics
A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanw...
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doaj-d6ca14f1545f4b869237c01e731abf6b2020-11-25T03:53:24ZengMDPI AGSensors1424-82202020-06-01203669366910.3390/s20133669Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown StatisticsLei Zhong0Yong Li1Wei Cheng2Yi Zheng3School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaA novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose the cognitive radar tracking method based on a particle filter by completely reconstructing the propagation and the update process with a cognitive structure. Moreover, we develop the cost-reference particle filter based on optimizing the cost function design according to the complicated system or environment with unknown statistics. With this method, the update of the estimation cost and variance arrives at the approximate optimization, and the estimation error can be more adjacent to corresponding low bounds. Simulations about the tracking implementation in unknown noise are utilized to demonstrate the superiority of the proposed algorithm to the existing methods in traditional radar.https://www.mdpi.com/1424-8220/20/13/3669cognitive radarparticle filtertarget trackingbayesian boundsnonlinear model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Zhong Yong Li Wei Cheng Yi Zheng |
spellingShingle |
Lei Zhong Yong Li Wei Cheng Yi Zheng Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics Sensors cognitive radar particle filter target tracking bayesian bounds nonlinear model |
author_facet |
Lei Zhong Yong Li Wei Cheng Yi Zheng |
author_sort |
Lei Zhong |
title |
Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics |
title_short |
Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics |
title_full |
Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics |
title_fullStr |
Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics |
title_full_unstemmed |
Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics |
title_sort |
cost-reference particle filter for cognitive radar tracking systems with unknown statistics |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-06-01 |
description |
A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose the cognitive radar tracking method based on a particle filter by completely reconstructing the propagation and the update process with a cognitive structure. Moreover, we develop the cost-reference particle filter based on optimizing the cost function design according to the complicated system or environment with unknown statistics. With this method, the update of the estimation cost and variance arrives at the approximate optimization, and the estimation error can be more adjacent to corresponding low bounds. Simulations about the tracking implementation in unknown noise are utilized to demonstrate the superiority of the proposed algorithm to the existing methods in traditional radar. |
topic |
cognitive radar particle filter target tracking bayesian bounds nonlinear model |
url |
https://www.mdpi.com/1424-8220/20/13/3669 |
work_keys_str_mv |
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1724478143134171136 |