Learning and Model-Based Approaches for Radar Target Detection

This paper addresses the active radar target detection problem using two different approaches: learning-based and model-based methods. The learning-based approach uses a convolutional neural network (CNN) to detect targets, while the model-based approach employs detection theory to design detectors. The detection theory framework is used to consider the subspace-based generalized likelihood ratio test (S-GLRT) and sample covariance matrix-based GLRT (SCM-GLRT) detectors. A new recursive implementation of the S-GLRT, called RS-GLRT, is proposed to address the possible ill-conditioning in the clutter cancelation stage of the S-GLRT detector. In addition, two new detectors are proposed by combining the detection theory and kernel theory frameworks, which enables the deployment of a richer feature space in the detection and improves the detection performance. A CNN-based detector is also presented, which provides a robust detector against diverse noise and clutter behaviors in various environments. To achieve this, a universal model is considered for receiver noise and clutter, known as the $\alpha $ -stable interference model, which allows for the correct definition of noise and clutter properties in the range of impulsive to Gaussian distributions. Extensive simulation results are presented, demonstrating the superior detection performance of the CNN-based method compared to the detection theory-based methods.