Multi-Target Detection in Sea Clutter with Convolutional Neural Network

Multi-target detection in the sea clutter of shipborne radar has significant value for the military. In order to improve target detection performance under sea clutter, and reduce the negative influence of adjacent targets, this paper compares technologies used for target detection, introduces deep learning idea into this field and proposes a multi-target detection model based on convolutional neural network. Through collection and analysis of the real data in radar, a network structure suitable for processing one-dimensional echo data was constructed by structure searching. Directional penalty method was developed to speed up the learning efficiency, and the network hyper-parameters were optimized to improve network performance, thus raised the signal to clutter ratio and achieved effective detection of multiple targets on the sea. Finally, the performance is verified based on the measured data by receiver operating characteristic curve and the signal to clutter ratio improvement factor, which shows the effectiveness of this model.