GAN-CNN-Based Moving Target Detector for Airborne Radar Systems

Conventional phased-array airborne radar accomplishes target detection tasks through adaptive filtering and constant false alarm techniques. However, it encounters significant performance degradation in scenarios characterized by nonhomogeneous clutter and low signal-to-noise ratio (SNR). This paper introduces an airborne radar target detection (ARTD) scheme based on a Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN), capitalizing on the distributional properties of targets and clutter in the Beam-Doppler (BD) domain. In contrast to conventional ARTD methods that rely on clutter suppression processing, the proposed approach centers on target and clutter classification. Specifically, the proposed scheme comprises two modules: the reconstruction network and the detection network. The reconstruction module employs an adversarial learning mechanism to reconstruct and denoise the BD-maps with high resolution, while the detection module extracts target information from the reconstructed BD-maps. Results from experimental assessments involving both simulated and actual radar echoes indicate that the proposed GAN-CNN-based detector outperforms the comparison methods in terms of detection capability and accuracy. Moreover, it exhibits a reduced false alarm rate, particularly in scenarios with low SNR.