Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/69572
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dc.contributor.authorSekar Sathiya Narayanan
dc.date.accessioned2017-02-17T02:08:22Z
dc.date.available2017-02-17T02:08:22Z
dc.date.issued2017
dc.identifier.citationSekar Sathiya Narayanan. (2017). Compressive sensing reconstruction algorithms using partially correct signal information. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/69572
dc.description.abstractCompressive 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.en_US
dc.format.extent172 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleCompressive sensing reconstruction algorithms using partially correct signal informationen_US
dc.typeThesis
dc.contributor.supervisorAnamitra Makuren_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeDoctor of Philosophy (EEE)en_US
dc.identifier.doi10.32657/10356/69572-
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