Abstract
We propose a deep learning architecture, dubbed Plug-and-play 2D ADMM-Net (PAN), by combining model-driven deep networks and data-driven deep networks for effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging with various signal-to-noise ratios (SNR) and incomplete data scenarios. First, a sparse observation model of 2D ISAR imaging is established, and a 2D ADMM algorithm is presented. On this basis, using the plug and play (PnP) technique, PnP 2D ADMM is proposed, by combining the 2D ADMM algorithm and the deep denoising network DnCNN. Then, we unroll and generalize the PnP 2D ADMM to the PAN architecture, in which all adjustable parameters in the reconstruction layers, denoising layers, and multiplier update layers are learned by end-to-end training through back-propagation. Experimental results showed that the PAN with a single parameter set can achieve noise-robust ISAR imaging with superior reconstruction performance on incomplete simulated and measured data under different SNRs.
Highlights
High-resolution inverse synthetic aperture radar (ISAR) transmits a wide band signal to achieve a high resolution range profile and synthesizes a virtual aperture through the motion of the target, to achieve a high resolution along azimuth direction
Well-focused ISAR imaging can be obtained from high signal-to-noise ratio (SNR) and complete echoes using a range-Doppler (RD) algorithm and the polar formatting algorithm (PFA) [3]
ISAR Imaging Based on plug and play (PnP) 2D alternating direction method of multipliers (ADMM)
Summary
High-resolution inverse synthetic aperture radar (ISAR) transmits a wide band signal to achieve a high resolution range profile and synthesizes a virtual aperture through the motion of the target, to achieve a high resolution along azimuth direction. In ISAR imaging, the model-driven methods expand the iterative steps of the sparse signal reconstruction method into a deep network with finite-layers, set the adjustable parameters as network parameters, and obtain their optimal values by network training. They output the focused image of an unknown target from the trained network. The subjective network design process lacks unified criterion and theoretical support, which makes it difficult to analyze the influence of network structure and parameter settings on the reconstruction performance
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