Multifrequency Compressed Sensing for 2-D Near-Field Synthetic Aperture Radar Image Reconstruction


This paper investigates a new multifrequency compressed sensing (CS) model for 2-D near-field microwave and millimeter-wave synthetic aperture radar (SAR) imaging system, which usually collects multifrequency sparse data. Spatial data of each frequency are represented as a hierarchical tree structure under a wavelet basis and spatial data of different frequencies are modeled as a joint structure, because they are highly correlated. Based on the developed multifrequency CS model, a new CS approach is proposed by exploiting both the intrafrequency and interfrequency correlations, and enriches the existing CS approaches for 2-D near-field microwave and millimeter-wave SAR image reconstruction from undersampled measurements. Combining a splitting Bregman update with a variation of the parallel Fast Iterative Shrinkage-Thresholding Algorithm-like proximal algorithm, the proposed CS approach minimizes a linear combination of five terms: a least squares data fitting, a multi-ℓ1 norm, a multitotal variation norm, a joint-sparsity ℓ21 norm, and a tree-sparsity overlapping ℓ21 norm. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and convergence speed.


Electrical and Computer Engineering

Keywords and Phrases

Compressed sensing; Forestry; Image processing; Image reconstruction; Iterative methods; Millimeter waves; Radar; Radar signal processing; Signal reconstruction; Synthetic aperture radar; Trees (mathematics); Compressive sensing; Different frequency; Hierarchical tree; Inter-frequency correlation; Iterative shrinkage-thresholding algorithms; Least-squares data-fitting; Linear combinations; Proximal algorithm; Radar imaging; 2-D radar imaging; Compressed sensing (CS); Joint sparsity; Microwave imaging; Millimeter-wave imaging; Near field; Synthetic aperture radar (SAR); Tree sparsity

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Article - Journal

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