Unknown wall parameters will cause the target position offset and image distortion for three-dimensional imaging of through-the-wall radar. In this paper, we propose an autofocusing imaging method of alternating learning of wall parameters and sparse coefficients based on the parametric diffraction tomography sparse model. The sparse coefficients reconstruction algorithm is unrolled a multilayer neural network for learning to update the sparse coefficients. The normalized mean square error is selected as the network loss function, and the batch gradient descent method is used to learn the network parameters. Subsequently, we still use this neural network to learn the variation of wall parameters to update the wall parameters, but the difference is to use the image quality evaluation function as the loss function. After several alternating iterations, we determine the sparse coefficients and wall parameters that minimize the loss function as the final result. The simulation results show that this proposed method can effectively eliminate the target position offset and image distortion, so that it can realize accurate estimation of wall parameters and autofocusing imaging.
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