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Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
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Sensors 2020, 20(13), 3669; https://doi.org/10.3390/s20133669
Received: 23 May 2020 / Revised: 26 June 2020 / Accepted: 28 June 2020 / Published: 30 June 2020
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: cognitive radar; particle filter; target tracking; bayesian bounds; nonlinear model cognitive radar; particle filter; target tracking; bayesian bounds; nonlinear model
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Zhong, L.; Li, Y.; Cheng, W.; Zheng, Y. Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics. Sensors 2020, 20, 3669.

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