Abstract:

When detecting weak targets, radar systems often encounter the interference from complex clutter in the field scene, leading to a high false alarm rate and a low detection rate when the traditional radar methods are employed. To address this issue, this paper proposes a clutter suppression method based on generative adversarial networks (GANs) to improve the detection performance of weak targets. This method designs a clutter identification network based on the characteristics of clutter in the range-Doppler domain. By adopting a divide-and-conquer strategy, a clutter suppression network is selected based on the clutter identification results. This method transforms the clutter suppression process into a mapping problem from the clutter-affected data domain to a clutter-free data domain. Experiments results show that the proposed method achieves a 10% improvement in detection rate in rain clutter scenarios, and reduce the false alarm rate by 90% in ground clutter scenarios.

Key words: weak target detection, clutter identification, clutter suppression, generative adversarial network (GAN)