采用卷积神经网络的海面多目标检测研究

对海雷达多目标检测在军事领域有着重要的应用价值。为了提高海杂波下的目标检测性能，减少临近目标的影响，本文引入深度学习思想，提出了采用卷积神经网络的多目标检测方法。通过雷达实测数据的分析与训练，构造适用于处理一维回波数据的网络模型，引入定向惩罚技术加快自适应学习效率，优化网络超参数提升网络性能，实现了回波数据信杂比的较大改善，完成了海面多目标的有效检测。最后，基于实测数据对该方法进行了性能验证，实验结果显示了本方法的有效性。 

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.