Siamese Sparse Reconstruction Network for Ionospheric Clutter Suppression in HFSWR

High frequency surface wave radar (HFSWR) suffers from performance degradation due to strong ionospheric clutter. We proposes a deep learning-based method, siamese sparse reconstruction network (SSRN), for ionospheric clutter suppression in HFSWR. The SSRN integrates sparse coding into a siamese encoder-decoder structure tailored for complex-valued radar data. The convolutional layers perform iterative approximation of the range-doppler spectrum degradation function. The RD spectrum reconstruction loss preserves phase characteristics. The enhancement in signal to clutter and noise ratio (SCNR) gain within the ionospheric clutter using the wavelet series oblique projection filter suppression algorithm is observed to be approximately 4 dB. Experiments demonstrate the advantages of SSRN in clutter suppression over traditional sparse reconstruction and deep learning methods. This data-driven approach combining deep learning and sparse coding holds promise for enhancing HFSWR clutter mitigation.