Range-Doppler Spectrograms-Based Graph-Relational Mapping for Clutter Rejection in HF Passive Radar

Clutter rejection is a key technique for high-frequency passive radar (HFPR). To solve this problem, the traditional signal processing methods have been used, which mainly depend on prior information of the feature differences between target and clutter in time, space, or frequency domain. As a new attempt to deep-mine the clutter feature automatically and reject it by only data-driven processing, a novel clutter rejection method based on graph-relational mapping using a deep learning network is proposed in this letter. In this method, the clutter rejection problem is turned into an image-to-image translation problem between the range-Doppler (RD) spectrograms before and after clutter rejection. A deep-learning-enabled image translation network (CycleGAN) is exploited to learn from training data of RD spectrograms and to establish the mapping relationship. When processing clutter rejection tasks, the trained network can automatically extract clutter features without prior information and save manpower. The performance evaluations of the novel clutter rejection method are also investigated, and the experimental results confirm that the proposed method can effectively reject clutter in HFPR.