基于ARDS-Net双路径融合的实测双极化HRRP非均匀样本舰船识别方法OA

To address the significant degradation of ship high resolution range profiles(HRRP)classification perfor-mance under low SNR caused by feature degradation,this study proposes an adaptive residual depthwise shrinkage net-work(ARDS-Net)that fuses handcrafted features and deep learning for ship recognition.Firstly,Total Variation(TV)denoising and Krogager polarization decomposition are performed on radar dual-polarization data to extract hybrid handc-rafted features integrating polarization statistical features and wavelet anti-noise features.Then,a residual shrinkage building unit with channel-wise thresholds(RSBU-CW)based on depthwise separable convolution is designed to sup-press noise via learned dynamic soft thresholds.Finally,focal loss is used to optimize the weights of hard samples under low SNR.Experiments on a measured dataset with sea clutter and electromagnetic interference show stable anti-noise per-formance from-10 to 10 dB,with accuracies of 52.94%(0 dB),73.05%(5 dB),and 91.11%(10 dB).Relying on the multi-dimensional anti-noise mechanism,the proposed method effectively improves the robustness of ship radar polariza-tion feature recognition under complex electromagnetic environments,and can provide technical support for engineering applications such as maritime security and ship monitoring.