Compressive sensing reconstruction algorithms using partially correct signal information
Compressive Sensing (CS) ensures the reconstruction of a sparse signal from a set of linear measurements that are fewer compared to the signal length. The sparse signal can be reconstructed using a convex relaxation algorithm such as Basis Pursuit (BP) or a Greedy Pursuit (GP) such as Backtrackin...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/69572 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Compressive Sensing (CS) ensures the reconstruction of a sparse signal from a set of linear
measurements that are fewer compared to the signal length. The sparse signal can be
reconstructed using a convex relaxation algorithm such as Basis Pursuit (BP) or a Greedy
Pursuit (GP) such as Backtracking Matching Pursuit (BMP). If some information regarding
the signal support (non-zero locations) is available in the form of Partially Known Support
(PKS), the same sparse signal can be recovered with higher accuracy. However, the size
and accuracy of the PKS varies depending upon the signal model and characteristics. A
generic PKS based reconstruction algorithm might work well in a particular scenario but
fail in another. This thesis focuses on developing PKS based reconstruction algorithms for
different scenarios wherein they make effective use of the available PKS. |
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