TR2016-091
Compressive Tomographic Radar Imaging with Total Variation Regularization
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- , "Compressive Tomographic Radar Imaging with Total Variation Regularization", International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa), DOI: 10.1109/CoSeRa.2016.7745712, pp. 120-123, September 2016.BibTeX TR2016-091 PDF
- @article{Liu2016sep,
- author = {Liu, Dehong and Kamilov, Ulugbek and Boufounos, Petros T.},
- title = {Compressive Tomographic Radar Imaging with Total Variation Regularization},
- journal = {International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa)},
- year = 2016,
- pages = {120--123},
- month = sep,
- doi = {10.1109/CoSeRa.2016.7745712},
- url = {https://www.merl.com/publications/TR2016-091}
- }
- , "Compressive Tomographic Radar Imaging with Total Variation Regularization", International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa), DOI: 10.1109/CoSeRa.2016.7745712, pp. 120-123, September 2016.
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Abstract:
We consider the problem of compressive imaging of a three-dimensional (3D) scene using multiple observations collected from parallel baselines, formed by monostatic sensors moving in space. In particular, we present a novel iterative imaging method based on the Omega-K algorithm with edgepreserving 3D total variation (TV) regularization. The method combines joint processing of multi-baseline data with TV minimization in a computationally efficient way, thus enabling highresolution imaging of the reflectivity map of the scene. We demonstrate the potential of our method through numerical evaluations on simulated data with noise.