Bayesian Hypothesis Testing for One Bit Compressed Sensing with Sensing Matrix Perturbation
نویسندگان
1 Department of Mechatronics Engineering, University of Tabriz, P.O. Box 5166614761, Tabriz, Iran
2 Department of Mechatronics Engineering, University of Tabriz, P.O. Box 5166614761, Tabriz, Iran
3 Rehabilitation Robotics Research Lab., Department of Mechatronics Engineering, University of Tabriz, P.O. Box 5166614761, Tabriz, Iran
doi
10.24200/sci.2017.4374چکیده
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.
