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Learning-Based Split Unfolding Framework for 3-D mmW Radar Sparse Imaging
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DOI：10.1109/TGRS.2022.3181174.png)
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Abstract
En 中文
The application of the compressed sensing (CS) method in the radar field enables the radar imaging system to satisfy both low data cost and high reconstruction quality; however, it is accompanied by enormous iterative operations and difficult adjustments of parameters. In this article, we propose a learning-based split unfolding framework, dubbed as split iterative sparse reconstruction network (SISR-Net), for near-field 3-D millimeter-wave (mmW) radar sparse imaging. First, a split iterative sparse reconstruction algorithm, i.e., SISRA, is proposed to theoretically guide the structure of the imaging framework. Subsequently, by combining the model-based CS method and data-driven deep learning method, SISR-Net is constructed by SISRA to produce 3-D mmW radar images efficiently with excellent explainability and generalization ability. Combining the radar-imaging kernel, echo-generation kernel, and the split Bregman method, the efficiency and stability of SISR-Net are guaranteed, and all the parameters are layer-varied and learned steadily by end-to-end training to improve the convergence and robustness of the imaging network. Simulated data and echo from a high-resolution mmW radar dataset 3DRIED are used to train and test the SISR-Net based on the Adam optimizer. For both simulation and extensive 3-D mmW radar-measured experiments, the proposed SISR-Net outperforms other state-of-the-art imaging methods in terms of imaging accuracy and generalization ability.
Keywords:
Radar imaging
Imaging
Radar
Image reconstruction
Sparse matrices
Synthetic aperture radar
Solid modeling
3-D holography
compressed sensing (CS)
deep unfolding framework
millimeter-wave imaging
radar imaging
sparse reconstruction
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W