Rotation Speed Estimation of SAR Ship Target Based on Complex-Valued Convolutional Long Short-Term Memory Network

Aiming at the three-dimensional rotation speed estimation task of a synthetic aperture radar (SAR) ship target, this paper proposes a complex-valued convolutional long short-term memory (CV-ConvLSTM) network. It can simultaneously perceive the time, space and frequency domain information of the complex SAR imagery sequence. All elements of convolutional long short-term memory (ConvLSTM) network including convolutional layer, activation function, input gate, forget gate, and output gate are extended to the complex domain. In order to verify the superiority of CV-ConvLSTM in frequency domain information perception over ConvLSTM. Experiments show that CV-ConvLSTM has higher estimation accuracy than ConvLSTM.