MLP-CFAR for improving coherent radar detectors robustness in variable scenarios

作者：

Highlights：

• Neural Networks based CFAR techniques are proposed in an improved coherent radar detector.

• A coherent detector using a unique CFAR is compared to the classical bank of CFAR techniques.

• The filter bank output is statistically analyzed to prove Gaussian CFARs unfeasibility.

• The proposed neural CFAR can be applied to any clutter distribution or detection strategy.

• A comparative study is carried out on a simulated scenario with complex target trajectories.

摘要

•Neural Networks based CFAR techniques are proposed in an improved coherent radar detector.•A coherent detector using a unique CFAR is compared to the classical bank of CFAR techniques.•The filter bank output is statistically analyzed to prove Gaussian CFARs unfeasibility.•The proposed neural CFAR can be applied to any clutter distribution or detection strategy.•A comparative study is carried out on a simulated scenario with complex target trajectories.

论文关键词：Radar detection,Neyman–Pearson detector,Neural Networks,MTI,MTD,CFAR

论文评审过程：Available online 25 February 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.12.055