Refocusing on SAR Ship Targets With Three-Dimensional Rotating Based on Complex-Valued Convolutional Gated Recurrent Unit
Qinglong Hua, Yun Zhang, Hongbo Li, Yicheng Jiang, Dan Xu
Abstract
This letter proposes a complex-valued convolutional gated recurrent unit (CV-ConvGRU) network for the three-dimensional rotation refocusing task of a synthetic aperture radar (SAR) ship target. To take advantage of the amplitude and phase information of complex SAR images, all elements of CV-ConvGRU, including the convolutional layer, activation function, update gate and reset gate, are extended to the complex domain. Based on CV-ConvGRU, a complex-valued SAR ship refocusing network (CV-SSRN) architecture is designed for refocusing experiments. To verify the robustness of the proposed CV-ConvGRU over ConvGRU on information perception, this letter also raises a real-valued SAR ship refocusing network (RV-SSRN), which has the same degree of freedom as CV-SSRN. Finally, experiments are carried out, and all results show the superiority of the proposed method on refocusing accuracy.