MRCS-Net: Multi-Radar Clustering Segmentation Networks for Full-Pulse Sequences - Tao Chen; Yu Lei; Limin Guo; Boyi Yang (Remote Sensing)
To facilitate the full-pulse sequence received by a radar reconnaissance receiver, this study proposed a clustering segmentation method for radar signals. Owing to the influence of the complex electromagnetic environment, the probability of the occurrence of time–frequency overlapping of signals increases, and the demand for signal localization and classification becomes higher. However, most existing studies have only classified and identified individual pulse signals and lack the ability to analyze signals for full pulses. This study proposed a multi-radar cluster-based segmentation network (MRCS-Net) for large time-length full-pulse signals. The network innovatively addresses the processing challenges of prolonged full-pulse signals and effectively achieves the classification and recognition of different pulses under time–frequency overlapping conditions. The proposed algorithm filters the signal with SincNet and then sequentially feeds the sequence into a long short-term memory network. Consequently, the outputs are clustered and segmented using multilayer perceptrons and classifiers. Experiments were conducted on six different types of radar signals. The results demonstrated that the proposed method exhibited lower segmentation error rate metric compared to other similar methods. Moreover, it outperformed other methods in terms of recognition performance.