Research on Intelligent Identification of Sea Surface Targets and Sea Clutter Based on Lightweight Network

Abstract

Aiming at the problem of sea clutter suppression and dim target detection in sea clutter, a sea clutter identification method based on detection sliding window convolutional neural network ( DSW-CNN ) model is proposed. Firstly, the characteristics of sea clutter are analyzed to obtain the characteristics that can distinguish sea clutter and target. Then, the fully polarized radar signal is converted into graph data, and the sea clutter is initially suppressed by using the spatial and temporal characteristics of the graph data. Finally, the proposed DSW-CNN model is used to extract and classify the feature of the graph data nodes, so as to realize the target identification in the background of sea clutter. The proposed method is simulated and analyzed by using experimental data. Experiments show that the proposed method can achieve a detection probability of 98 % when the target signal-to-clutter ratio is less than 12 dB, and the parameter scale of the lightweight model is reduced to 10 % of the original model. The proposed model provides some support for sea clutter suppression theory and engineering implementation.