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• W2067878805 abstract "Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed with an underdetermined linear measurement model. However, in reality there is a mismatch between the assumed and the actual bases due to factors such as discretization of the parameter space defining basis components, sampling jitter in A/D conversion, and model errors. Due to this mismatch, a signal may not be sparse in the assumed basis, which causes significant performance degradation in sparse reconstruction algorithms. To eliminate the mismatch problem, this paper presents a novel perturbed orthogonal matching pursuit (POMP) algorithm that performs controlled perturbation of selected support vectors to decrease the orthogonal residual at each iteration. Based on detailed mathematical analysis, conditions for successful reconstruction are derived. Simulations show that robust results with much smaller reconstruction errors in the case of perturbed bases can be obtained as compared to standard sparse reconstruction techniques." @default.
• W2067878805 created "2016-06-24" @default.
• W2067878805 creator A5036752295 @default.
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• W2067878805 date "2013-12-01" @default.
• W2067878805 modified "2023-09-30" @default.
• W2067878805 title "Perturbed Orthogonal Matching Pursuit" @default.
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• W2067878805 doi "https://doi.org/10.1109/tsp.2013.2283840" @default.
• W2067878805 hasPublicationYear "2013" @default.
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