Scanning Radar Target Reconstruction Using Deep Convolutional Neural Network

Target reconstruction is one of the most important missions in the fields of radar signal processing. In this paper, we propose a new deep learning-based approach to reconstruct the target information from the scanning radar returns. Unlike the traditional analytical methods, a deep neural network with a topology of linear chains of convolutional layers is designed, and the input radar signals will be learned layer by layer through the network, which a direct map from the radar echo to the reflectivity function of the targets is obtained during the learning procedure. Finally, we can get the optimal deep learning network as the reconstructing map to recover the scanning radar target information effectively. Simulation results have shown the superiority of the proposed method under different target scenes.