Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

2018/12/06 by Shiyong Li, Moeness G. Amin, Li, Shiyong +5
Engineering · #Advanced SAR Imaging Techniques #FOS: Electrical engineering #Microwave Imaging and Scattering Analysis #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1812.02366

openalex publication_date 2018/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Recent progress in compressive sensing states the importance of exploiting intrinsic structures in sparse signal reconstruction. In this letter, we propose a Markov random field (MRF) prior in conjunction with fast iterative shrinkagethresholding algorithm (FISTA) for image reconstruction. The MRF prior is used to represent the support of sparse signals with clustered nonzero coefficients. The proposed approach is applied to the inverse synthetic aperture radar (ISAR) imaging problem. Simulations and experimental results are provided to demonstrate the performance advantages of this approach in comparison with the standard FISTA and existing MRF-based methods.

Citations

• Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

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