基于混合型复数域卷积神经网络的三维转动舰船目标识别

Because the ship targets are in a non-stationary motion state of random swing, conventional synthetic aperture radar(SAR) imaging processing will make the targets defocused and azimuth blurred, resulting in the recognition accuracy of three-dimensional rotating ship. This paper proposes a mixed-type complex-valued convolutional neural network (Mix-CV-CNN) and derives the Mix-CV-CNN forward propagation and backpropagation algorithms. The three-dimensional rotating target has residual phase information after SAR imaging processing. The Mix-CV-CNN could make full use of the amplitude and phase information of the complex SAR image and could better complete the recognition of SAR three-dimensional rotating targets without target refocusing. The experimental results show that Mix-CV-CNN has improved recognition performance compared with the real-valued convolutional neural network(RV-CNN) with the same degree of freedom. The average accuracy is increased by 3.85%.