Forward-looking Imaging Method of Airborne Radar Based on Alternating Direction Multiplier Network

Real aperture super-resolution technology has been widely used in the field of radar forward-looking imaging, but most of the current iterative methods face the problems of difficult parameter selection and time-consuming iterative reconstruction. In this paper, a forward-looking imaging method of airborne radar based on alternating direction multiplier network is proposed. In this method, the forward-looking imaging is constructed as a deconvolution problem with sparse constraints, and the iterative solution process of separating variables by alternating direction multiplier method (ADMM) is mapped into a deep neural network, namely ADMM-Net (ADMMN). After training, ADMMN can learn the optimal parameters under limited network depth, so as to improve the azimuth resolution of radar. Experimental results show that, compared with the traditional iterative algorithm, ADMMN can achieve super-resolution forward-looking imaging in less time.