IFNet: Deep Imaging and Focusing for Handheld SAR with Millimeter-wave Signals
CoRR(2024)
Abstract
Recent advancements have showcased the potential of handheld millimeter-wave
(mmWave) imaging, which applies synthetic aperture radar (SAR) principles in
portable settings. However, existing studies addressing handheld motion errors
either rely on costly tracking devices or employ simplified imaging models,
leading to impractical deployment or limited performance. In this paper, we
present IFNet, a novel deep unfolding network that combines the strengths of
signal processing models and deep neural networks to achieve robust imaging and
focusing for handheld mmWave systems. We first formulate the handheld imaging
model by integrating multiple priors about mmWave images and handheld phase
errors. Furthermore, we transform the optimization processes into an iterative
network structure for improved and efficient imaging performance. Extensive
experiments demonstrate that IFNet effectively compensates for handheld phase
errors and recovers high-fidelity images from severely distorted signals. In
comparison with existing methods, IFNet can achieve at least 11.89 dB
improvement in average peak signal-to-noise ratio (PSNR) and 64.91
in average structural similarity index measure (SSIM) on a real-world dataset.
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