Recognition of Micromotion Space Targets at Low SNR Based on Complex-Valued Time Convolutional Attention Denoising Recognition Network

For a micro-motion space target, its narrowband radar cross section (RCS) series reflects characteristics of target shape and motion. In practical scenarios, however, the RCS series of distant targets with weak scattering coefficients suffer from low signal-to-noise ratio (SNR), and performing separate noise suppression and recognition purely on the amplitude results in degraded recognition performance. To tackle this issue, an end-to-end complex-valued (CV) time convolutional attention denoising recognition network, dubbed as CV-TCANet, is proposed. Specifically, the denoising module captures temporal correlation by the CV attention mechanism and calculates the noise mask for denoising; and the recognition module utilizes the CV temporal convolutional network (CV-TCN) for feature extraction and recognition. In addition, a hybrid loss is designed to realize the integration of denoising and recognition, thus preserving target information while denoising and improving the recognition accuracy. Experimental results have proved that the proposed method could achieve satisfying recognition performance at low SNR.