A New Multi-Frequency Compressed Sensing Model for 2-D Near-Field Synthetic Aperture Radar


A new multi-frequency compressed sensing (CS) model is introduced for the 2-D near-field microwave and millimeter-wave synthetic aperture radar (SAR) image reconstruction from under-sampled measurements. The near-filed SAR imaging system usually collects multi-frequency sparse data, where each frequency data can be represented as a hierarchical tree structure under a wavelet basis and different frequency data can be modeled as a joint structure because it's highly correlated. A new multi-frequency CS model, by integrating the tree-sparsity and joint-sparsity together, is proposed to exploit the structural dependencies of the multi-frequency SAR sparse data. In order to solve the corresponding constrained minimization problem, a multi-frequency CS approach is introduced by using a splitting Bregman update with a variation of the parallel Fista-like proximal algorithm. A corrosion-under-paint example demonstrates that the proposed multi-frequency CS model outperforms the conventional model, and the new multi-frequency CS approach enables us to further reduce the number of measurements required to stably recover SAR images of targets and better differentiate true SAR images from recovery artifacts.

Meeting Name

2016 8th International Conference on Wireless Communications and Signal Processing, WCSP (2016: Oct. 13-15, Yangzhou, China)


Electrical and Computer Engineering


National Science Foundation (U.S.)


This work was supported by the National Natural Science Foundation of China (61371049 & 60871056).

Keywords and Phrases

Compressed sensing; Constrained optimization; Forestry; Image processing; Image reconstruction; Millimeter waves; Radar; Radar imaging; Radar measurement; Radar signal processing; Signal processing; Signal reconstruction; Trees (mathematics); Wireless telecommunication systems; Compressive sensing; Constrained minimization problem; Conventional modeling; Different frequency; Hierarchical tree; Highly-correlated; Proximal algorithm; Synthetic aperture radar (SAR) images; Synthetic aperture radar

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


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© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Oct 2016