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

Research Center/Lab(s)

Intelligent Systems Center

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

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Apr 2017