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Enhanced local sparsity coefficient-based sea-surface floating target detection

Due to the varying sea clutter characteristics and weak target echo characteristics, sea surface small floating target detection becomes a difficult task for maritime radars. Among lots of detection methods, feature-based detection method is an effective way to detect this type of targets. The combination of multiple different features between sea clutter and targets can effectively improve the detection performance. However, convexhull learning algorithm used by traditional feature-based detectors is limited by three-dimensional feature space which causes the performance loss. Therefore, in this paper, an enhanced local sparsity coefficient-based detection method is proposed to combine the seven existing features in the seven-dimensional feature space and a simple threshold decision is utilized for target detection. To verify the detection performance of the proposed method, the recognized and measured sea clutter database is used, and the experimental result of the proposed method is compared with several existing feature-based detection methods. The results show that the proposed detection method attains competitive detection performance with fast decision.