Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch
We present compressed sensing (CS) synthetic aperture radar (SAR) moving target imaging in the presence of dictionary mismatch. Unlike existing work on CS SAR moving target imaging, we analyze the sensitivity of the imaging process to the mismatch and present an iterative scheme to cope with diction...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2013/142602 |
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| author | Ahmed Shaharyar Khwaja Muhammad Naeem Alagan Anpalagan |
| author_facet | Ahmed Shaharyar Khwaja Muhammad Naeem Alagan Anpalagan |
| author_sort | Ahmed Shaharyar Khwaja |
| collection | DOAJ |
| description | We present compressed sensing (CS) synthetic aperture radar (SAR) moving target imaging in the presence of dictionary mismatch. Unlike existing work on CS SAR moving target imaging, we analyze the sensitivity of the imaging process to the mismatch and present an iterative scheme to cope with dictionary mismatch. We analyze and investigate the effects of mismatch in range and azimuth positions, as well as range velocity. The analysis reveals that the reconstruction error increases with the mismatch and range velocity mismatch is the major cause of error. Instead of using traditional Laplacian prior (LP), we use Gaussian-Bernoulli prior (GBP) for CS SAR imaging mismatch. The results show that the performance of GBP is much better than LP. We also provide the Cramer-Rao Bounds (CRB) that demonstrate theoretically the lowering of mean square error between actual and reconstructed result by using the GBP. We show that a combination of an upsampled dictionary and the GBP for reconstruction can deal with position mismatch effectively. We further present an iterative scheme to deal with the range velocity mismatch. Numerical and simulation examples demonstrate the accuracy of the analysis as well as the effectiveness of the proposed upsampling and iterative scheme. |
| format | Article |
| id | doaj-art-6b6c976ee64244d4a0e40ea7e4845ba1 |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-6b6c976ee64244d4a0e40ea7e4845ba12025-08-20T03:34:00ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772013-01-01201310.1155/2013/142602142602Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary MismatchAhmed Shaharyar Khwaja0Muhammad Naeem1Alagan Anpalagan2Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, CanadaDepartment of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, CanadaDepartment of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, CanadaWe present compressed sensing (CS) synthetic aperture radar (SAR) moving target imaging in the presence of dictionary mismatch. Unlike existing work on CS SAR moving target imaging, we analyze the sensitivity of the imaging process to the mismatch and present an iterative scheme to cope with dictionary mismatch. We analyze and investigate the effects of mismatch in range and azimuth positions, as well as range velocity. The analysis reveals that the reconstruction error increases with the mismatch and range velocity mismatch is the major cause of error. Instead of using traditional Laplacian prior (LP), we use Gaussian-Bernoulli prior (GBP) for CS SAR imaging mismatch. The results show that the performance of GBP is much better than LP. We also provide the Cramer-Rao Bounds (CRB) that demonstrate theoretically the lowering of mean square error between actual and reconstructed result by using the GBP. We show that a combination of an upsampled dictionary and the GBP for reconstruction can deal with position mismatch effectively. We further present an iterative scheme to deal with the range velocity mismatch. Numerical and simulation examples demonstrate the accuracy of the analysis as well as the effectiveness of the proposed upsampling and iterative scheme.http://dx.doi.org/10.1155/2013/142602 |
| spellingShingle | Ahmed Shaharyar Khwaja Muhammad Naeem Alagan Anpalagan Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch International Journal of Antennas and Propagation |
| title | Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch |
| title_full | Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch |
| title_fullStr | Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch |
| title_full_unstemmed | Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch |
| title_short | Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch |
| title_sort | analysis of moving object imaging from compressively sensed sar data in the presence of dictionary mismatch |
| url | http://dx.doi.org/10.1155/2013/142602 |
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