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Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this paper, the LRSD-ADMM-net is proposed to achieve simultaneous super-resolution imaging and target detection for forward-looking scanning radar. Firstly, low-rank and sparse constraints as regularization norms are incorporated to establish objective function, and the alternating direction multiplier method (ADMM) is used to solve simultaneous super-resolution and target detection problems. Then, the solving process is expanded into a neural network, where the weight parameters of each level are obtained through adaptive learning. At last, experiments are conducted to verify the effectiveness of the proposed method.

LRSD-ADMM-NET : Simultaneous Super-resolution Imaging and Target Detection for Forward-looking Scanning Radar