HFSWR Clutter Suppression and Target Detection Method Based on RRN and Exponential Weight Control

This letter presents a novel method for clutter suppression and target detection on the range-Doppler (RD) spectrum of a high-frequency surface wave radar (HFSWR). The approach replaces the traditional classification layer of the residual neural network (ResNet) with a regression layer, forming the residual regression network (RRN). Subsequently, a target-embedded dataset is constructed using real measurement data and supplemented simulation data to train the network in learning the characteristics of targets, clutter, and noise interference in the RD spectrum of HFSWR, thus generating a spectrum containing only target information. Introducing an exponential weight control factor, the network output is exponentially weighted and Hadamard multiplied with the original input’s spectrum to obtain the final spectrum for detection. This final spectrum is then fed into the constant false alarm rate (CFAR) detector for further analysis. Through the verification of multiple datasets of target-embedded data and measured data and multiple experimental groups, this method demonstrates superior clutter suppression and target detection performance compared with the existing methods. Operators can adjust the suppression level by modifying the exponential weight control factor and optimize detection performance by tuning the control factor and CFAR detector parameters according to specific operational requirements.