A Bayesian CFAR detector for interference control in Weibull clutter

Zhang, Baiqiang; Xie, Junhao; Zhou, Wei

doi:10.1016/j.dsp.2020.102781

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…However, this information is not comprehensive enough to accurately describe the actual clutter environment. In addition, some scholars improve the detector itself to reduce the CFAR loss, such as the estimator based on zlog(z) [13], the quadruple-order statistics-based CFAR algorithm [14], the Bayesian CFAR detector [15]. But they do not fully use the prior knowledge of environmental clutter.…”

Section: Related Workmentioning

confidence: 99%

A CFAR Detection Algorithm Based on Clutter Knowledge for Cognitive Radar

LIU

Wang

et al. 2023

IEICE Trans. Fundamentals

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Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.

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Section: Related Workmentioning

confidence: 99%