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

• 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.

This paper tackles the detection of radar targets with unknown Doppler shift in presence of clutter. A Neural Network based Constant False Alarm Rate (CFAR) technique is proposed for adapting the detection threshold in an improved architecture based on the Generalized Likelihood Ratio (GLR) detector. Detection schemes based on Doppler processors (Moving Target Indicator (MTI) and Moving Target Detector (MTD)) and conventional CFAR detectors are considered as reference. In these reference solutions, interference is assumed Gaussian and white at the output of each Doppler filter, so conventional incoherent CFAR detectors are applied. The outputs of the CFAR detectors are combined using an OR operation to decide the presence of a target if, at least, one of the CFARs declares it. As a result, the PFAPFA is higher than the desired one, as we prove. In this paper, an improved detector is presented that combines the following features: a better approximation to the Neyman–Pearson detector based on the GLR (selecting the maximum filter bank output), and a unique CFAR detector applied to the squared modulus of the maximum filter bank output. Due to the non-linear nature of the maximum function, conventional CFAR detectors are not suitable. A Neural Network CFAR solution is proposed. A general design method is presented. Results prove that the designed CFAR allows the exploitation of the better detection capabilities of the detector based on the maximum function, providing a higher probability of detection while fulfilling the probability of false alarm requirement. The proposed method can be extended to other detection strategies and radar scenarios.