Please use this identifier to cite or link to this item:
https://doi.org/10.3390/rs8110929
DC Field | Value | |
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dc.title | Sparsity-inducing super-resolution passive radar imaging with illuminators of opportunity | |
dc.contributor.author | Zhang, S | |
dc.contributor.author | Zhang, Y | |
dc.contributor.author | Wang, W.-Q | |
dc.contributor.author | Hu, C | |
dc.contributor.author | Yeo, T.S | |
dc.date.accessioned | 2020-10-22T02:46:55Z | |
dc.date.available | 2020-10-22T02:46:55Z | |
dc.date.issued | 2016 | |
dc.identifier.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 | |
dc.identifier.issn | 20724292 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/178849 | |
dc.description.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. | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20201031 | |
dc.subject | Compressed sensing | |
dc.subject | Imaging techniques | |
dc.subject | Optical resolving power | |
dc.subject | Radar | |
dc.subject | Radar systems | |
dc.subject | Signal reconstruction | |
dc.subject | Signal to noise ratio | |
dc.subject | Tracking radar | |
dc.subject | Compressive sensing | |
dc.subject | Illuminator of opportunity (IO) | |
dc.subject | Passive radar imaging | |
dc.subject | Sparsity-inducing | |
dc.subject | Super resolution | |
dc.subject | Radar imaging | |
dc.type | Article | |
dc.contributor.department | ELECTRICAL AND COMPUTER ENGINEERING | |
dc.description.doi | 10.3390/rs8110929 | |
dc.description.sourcetitle | Remote Sensing | |
dc.description.volume | 8 | |
dc.description.issue | 11 | |
dc.description.page | 929 | |
Appears in Collections: | Elements Staff Publications |
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