Abstract
Synthetic aperture radar (SAR) imaging has developed rapidly in recent years. Although the traditional sparse optimization imaging algorithm has achieved effective results, its shortcomings are slow imaging speed, large number of parameters, and high computational complexity. To solve the above problems, an end-to-end SAR deep learning imaging algorithm is proposed. Based on the existing SAR sparse imaging algorithm, the SAR imaging model is first rewritten to the SAR complex signal form based on the real-value model. Second, instead of arranging the two-dimensional echo data into a vector to continuously construct an observation matrix, the algorithm only derives the neural network imaging model based on the iteration soft threshold algorithm (ISTA) sparse algorithm in the two-dimensional data domain, and then reconstructs the observation scene through the superposition and expansion of the multi-layer network. Finally, through the experiment of simulation data and measured data of the three targets, it is verified that our algorithm is superior to the traditional sparse algorithm in terms of imaging quality, imaging time, and the number of parameters.
Highlights
In order to solve the problems of low imaging quality, excessive parameter settings and difficulty in parameter tuning of traditional Synthetic aperture radar (SAR) sparse imaging methods, we proposed a novel end-to-end SAR sparse imaging method based on a neural network
An end-to-end SAR deep learning imaging method based on a sparse optimization iterative algorithm was proposed
Referring to the existing SAR sparse imaging algorithm, the SAR imaging model was first rewritten into an SAR complex signal form based on the real-value model
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. To solve the problems of parameter adjustment, high computational complexity with multiple iterations, and one-dimensional echo data in traditional compressed sensing imaging methods, this paper proposes an end-to-end deep learning network imaging method. The algorithm only performs imaging processing in the two-dimensional data domain and derives it into a neural network imaging model based on iteration soft threshold algorithm (ISTA) sparse algorithm, instead of arranging two-dimensional echo data into a vector to continuously construct an observation matrix. This can greatly reduce the computational cost and make sparse imaging of large-scale scenes possible.
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