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• W2071692493 abstract "In this paper, we consider the theoretical bound of the probability of error in compressed sensing (CS) with the Bayesian approach. In the detection problem, the signal is sparse and is reconstructed from a compressed measurement vector. Utilizing the oracle estimator in CS, we provide a theoretical bound of the probability of error when the noise in CS is white Gaussian noise (WGN). We show that without any additional information in CS, the probability of error obtained using the signal reconstructed by four recovery algorithms: the basis pursuit denoising (BPDN) algorithm, the Dantzig selector, the orthogonal matching pursuit (OMP) method and the compressive sampling matching pursuit (CoSaMP) algorithm is always larger than the derived theoretical bound. Simulation results demonstrate the effectiveness of our result." @default.
• W2071692493 created "2016-06-24" @default.
• W2071692493 creator A5049506273 @default.
• W2071692493 creator A5074063024 @default.
• W2071692493 date "2012-05-01" @default.
• W2071692493 modified "2023-10-16" @default.
• W2071692493 title "The detection bound of the probability of error in compressed sensing using Bayesian approach" @default.
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• W2071692493 doi "https://doi.org/10.1109/iscas.2012.6271831" @default.
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