Document Type: Research Note
Department of Electrical and Computer engineering, Qom university of technology, Qom, Iran.
School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, 3122 Australia
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
This paper proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypothesis test, while the amplitude estimator uses an ML estimator which is obtained by solving a convex optimization problem. Simulation results show that Bayesian hypothesis testing in combination with the ML estimator has more reconstruction accuracy than that of only an ML estimator and also has less computational complexity.