Abstract
Sparsity-based synthetic aperture radar (SAR) imaging has attracted much attention since it has potential advantages in improving the image quality and reducing the sampling rate. However, it is vulnerable to deliberate blanket disturbance, especially wideband noise interference (WBNI), which severely damages the imaging quality. This paper mainly focuses on WBNI suppression for SAR imaging from a new perspective—sparse recovery. We first analyze the impact of WBNI on signal reconstruction by deducing the interference energy projected on the real support set of the signal under different observation parameters. Based on the derived results, we propose a novel WBNI suppression algorithm based on dechirping and double subspace extraction (DDSE), where the signal of interest (SOI) is reconstructed by exploiting the known geometric prior and waveform prior, respectively. The experimental results illustrate that the DDSE-based WBNI suppression algorithm for sparsity-based SAR imaging is effective and outperforms the other algorithms.
Highlights
Synthetic aperture radar (SAR) is an active remote sensing modality for real-time information acquisition
To verify the performance of wideband noise interference (WBNI) suppression for sparsity-based SAR imaging based on the proposed algorithm in this paper, we carried out multiple experiments with simulated data
It can be seen that the running time of our proposed dechirping and double subspace extraction (DDSE) algorithm was at a minimum under low sparsity conditions, but it increased with the sparsity just like adaptive compressed sampling (ACS), while the Bayesian pursuit denoising (BPDN) and the block sparse Bayesian learning (BSBL) changed little
Summary
Synthetic aperture radar (SAR) is an active remote sensing modality for real-time information acquisition. As a kind of semi-parametric method, is state-of-the-art, especially in terms of reducing signal distortion It can be considered as an optimization problem of reconstructing a few coefficients with a given dictionary. From the aspect of sensing recovery, basis pursuit denoising (BPDN) [24] takes the disturbance component into account in the reconstruction model and weakens the noise by decomposing the observed data into signal and residual components It is a common method, provided that the signal has been contaminated but the SNR is not quite low. We focus on the suppression of incoherent wideband noise interference for SAR imaging from the perspective of sparse signal processing.
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