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https://doi.org/10.3390/rs8110929
Title: | Sparsity-inducing super-resolution passive radar imaging with illuminators of opportunity | Authors: | Zhang, S Zhang, Y Wang, W.-Q Hu, C Yeo, T.S |
Keywords: | Compressed sensing Imaging techniques Optical resolving power Radar Radar systems Signal reconstruction Signal to noise ratio Tracking radar Compressive sensing Illuminator of opportunity (IO) Passive radar imaging Sparsity-inducing Super resolution Radar imaging |
Issue Date: | 2016 | Citation: | Zhang, S, Zhang, Y, Wang, W.-Q, Hu, C, Yeo, T.S (2016). Sparsity-inducing super-resolution passive radar imaging with illuminators of opportunity. Remote Sensing 8 (11) : 929. ScholarBank@NUS Repository. https://doi.org/10.3390/rs8110929 | Rights: | Attribution 4.0 International | Abstract: | 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. © 2016 by the authors. | Source Title: | Remote Sensing | URI: | https://scholarbank.nus.edu.sg/handle/10635/178849 | ISSN: | 20724292 | DOI: | 10.3390/rs8110929 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
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