Physical model-driven deep networks for through-the-wall radar imaging

In order to merge the advantages of the traditional compressed sensing (CS) methodology and the data-driven deep network scheme, this paper proposes a physical model-driven deep network, termed CS-Net, for solving target image reconstruction problems in through-the-wall radar imaging. The proposed method consists of two consequent steps. First, a learned convolutional neural network prior is introduced to replace the regularization term in the traditional iterative CS-based method to capture the redundancy of the radar echo signal. Moreover, the physical model of the radar signal is used in the data consistency layer to encourage consistency with the measurements. Second, the iterative CS optimization is unrolled to yield a deep learning network, where the weight, regularization parameter, and the other parameters are learnable. A quantity of training data enables the network to extract high-dimensional characteristics of the radar echo signal to reconstruct the spatial target image. Simulation results demonstrated that the proposed method can achieve accurate target image reconstruction and was superior to the traditional CS method, in terms of mean squared error and the target texture details.

Automated summary

This paper proposes a physical model-driven deep network approach, represented by CS-Net, to solve target image reconstruction problems in through-the-wall radar imaging. By introducing a convolutional neural network prior to capture the redundancy of radar echo signals and using the physical model of radar signals to encourage data consistency, the method is able to extract high-dimensional features from a large amount of training data for reconstructing spatial target images effectively. Simulation results show that the proposed method outperforms traditional CS methods in terms of accurate target image reconstruction and preservation of target texture details.