The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
The exploitation of sparsity has significantly advanced the field of radar imaging over the last few decades, leading to substantial improvements in the resolution and quality of the processed images. More recent developments in compressed sensing (CS) suggest that statistical sparsity can lead to f...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/84674 http://hdl.handle.net/10220/41933 |
| Summary: | The exploitation of sparsity has significantly advanced the field of radar imaging over the last few decades, leading to substantial improvements in the resolution and quality of the processed images. More recent developments in compressed sensing (CS) suggest that statistical sparsity can lead to further performance benefits by imposing sparsity as a statistical prior on the considered signal. In this article, a comprehensive survey is made of recent progress on statistical sparsity based techniques for various radar imagery applications. |
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