Cost-Reference Particle Filters and Fusion of Information

Bugallo, Mónica F.; Maiz, Cristina S.; Mı́guez, Joaquı́n; Djurić, Petar M.

doi:10.1109/dsp.2009.4785936

Cited by 1 publication

(1 citation statement)

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“…In [ 30 ], a particle selection algorithm was proposed and analyzed for implementation with parallel computing devices and to circumvent the main drawback of the conventional resampling techniques. Authors of [ 31 ] melded random measures of two or more cost-reference particle filters to obtain a fused random measure that combines the information from the individual cost-reference particle filters. However, there is no further study on the optimization of the cost function, especially when the tracking structure can be adaptive.…”

Section: Introductionmentioning

confidence: 99%

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

Zhong

Cheng

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%