A robust adaptive weighted CFAR detector based on truncated statistics

Renhong Xie; Junfeng Wei; Xing Wang; Bohao Dong; Peng Li; Yibin Rui

doi:10.1117/12.2581348

20 January 2021 A robust adaptive weighted CFAR detector based on truncated statistics

Renhong Xie, Junfeng Wei, Xing Wang, Bohao Dong, Peng Li, Yibin Rui

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171913 (2021) https://doi.org/10.1117/12.2581348
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. One or more outliers will appear in the reference cell under the multiple strong interferences situation, and the clutter power estimation will increase, which will affect the detection threshold calculation, the detection probability of CFAR detectors decrease and the alarm rates increase significantly. This paper proposes an adaptive weighted truncation statistic CFAR (AWTS-CFAR) algorithm and achieves good performance. By improving the truncation process, the truncated larger value is adaptively weighted with the smaller value in the reference cell. Since AWTS-CFAR makes the larger value in the reference cell also participate in the calculation of the background clutter power estimation, even if the truncation threshold is selected to be smaller, AWTS-CFAR will not cause too much loss of constant false alarm, and will suppress clutter edge effect as much as possible in the clutter edge environment.

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Renhong Xie, Junfeng Wei, Xing Wang, Bohao Dong, Peng Li, and Yibin Rui "A robust adaptive weighted CFAR detector based on truncated statistics", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171913 (20 January 2021); https://doi.org/10.1117/12.2581348

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KEYWORDS

Target detection

Environmental sensing

Detection and tracking algorithms

Sensors

Data modeling

Statistical analysis

Monte Carlo methods

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