Maritime Radar Target Detection Model Self-Evolution Based on Semisupervised Learning

Radar target detection in sea clutter aims to effectively discern the presence of maritime targets within the current radar echo. With the advancement of deep learning technology, an ever-growing number of researchers are turning to neural networks as the foundation for constructing detection models. These sophisticated neural models demonstrate promising performance in public datasets. On this basis, we propose an innovative self-evolution framework for radar target detection models using semisupervised learning (SesuL). The proposed approach aims to enhance the performance of the detection model under various radar conditions. Notably, this research presents the first-ever attempt within the literature to introduce such an approach for pulse-compression radar. To bolster the performance of the proposed model, novel techniques are introduced for sample selection, sample augmentation, and model optimization. Experimental findings provide compelling evidence supporting the superiority of the proposed method in terms of detection performance and robustness under unknown conditions, surpassing existing techniques. In light of practical deployment considerations, future efforts should be directed toward investigating the fusion of radar and other sensors, such as visible light, to enhance the detection performance.