Performance analysis of MLP-based radar detectors in Weibull-distributed clutter with respect to target doppler frequency

In this paper, a Multilayer Perceptron (MLP) is proposed as a radar detector of known targets in Weibull-distributed clutter. The MLP is trained in a supervised way using the Levenberg-Marquardt back-propagation algorithm to minimize the Mean Square Error, which is able to approximate the Neyman-Pearson detector. Due to the impossibility to find analytical expressions of the optimum detector for this kind of clutter, a suboptimum detector is taken as reference, the Target Sequence Known A Priori (TSKAP) detector. Several sizes of MLP are considered, where even MLPs with very low sizes are able to outperform the TSKAP detector. On the other hand, a sensitivity study with respect to target parameters, as its doppler frequency, is made for different clutter conditions. This study reveals that both detectors work better for high values of target doppler frequency and one-lag correlation coefficient of the clutter. But the most important conclusion is that, for all the cases of the study, the MLP-based detector outperforms the TSKAP one. Moreover, the performance improvement achieved by the MLP-based detector is higher for lower probabilities of false alarm than for higher ones.