Next Article in Journal
Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations
Next Article in Special Issue
Joint Time-Frequency Signal Processing Scheme in Forward Scattering Radar with a Rotational Transmitter
Previous Article in Journal / Special Issue
Interference Mitigation Achieved with a Reconfigurable Stepped Frequency GPR System
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(11), 929;

Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity

Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Electronics and Information School, Beijing Institute of Technology, Beijing 100081, China
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
Author to whom correspondence should be addressed.
Academic Editors: Daniele Riccio, Francesco Soldovieri, Raffaele Persico, Magaly Koch and Prasad S. Thenkabail
Received: 9 May 2016 / Revised: 19 August 2016 / Accepted: 19 October 2016 / Published: 8 November 2016
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
Full-Text   |   PDF [701 KB, uploaded 8 November 2016]   |  


Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current passive radar imaging techniques. However, a large rotation angle demands a long observation time, which cannot be implemented for actual passive radar system. To overcome this disadvantage, this paper proposes a super-resolution passive radar imaging framework with a sparsity-inducing compressed sensing (CS) technique, which allows for fewer IOs and a smaller rotation angle. In the proposed imaging framework, the sparsity-based passive radar imaging is modeled mathematically, and the spatial frequencies and amplitudes of different scatterers on the target are recovered by the log-sum penalty function-based CS reconstruction algorithm. In doing so, a super-resolution passive radar imagery is obtained by the frequency searching approach. Simulation results not only validate that the proposed method outperforms existing super-resolution algorithms, such as ESPRIT and RELAX, especially in the cases with low signal-to-noise ratio (SNR) and limited number of measurements, but also have shown that our proposed method can perform robust reconstruction no matter if the target is on grid or not. View Full-Text
Keywords: sparsity-inducing; super-resolution; passive radar imaging; illuminator of opportunity (IO); compressed sensing (CS) sparsity-inducing; super-resolution; passive radar imaging; illuminator of opportunity (IO); compressed sensing (CS)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhang, S.; Zhang, Y.; Wang, W.-Q.; Hu, C.; Yeo, T.S. Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity. Remote Sens. 2016, 8, 929.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top