TY - JOUR
ID - 4374
TI - Bayesian Hypothesis Testing for One Bit Compressed Sensing with Sensing Matrix Perturbation
JO - Scientia Iranica
JA - SCI
LA - en
SN - 1026-3098
AU - Zayyani, H.
AU - Korki, M.
AU - Marvasti, F.
AD - Department of Electrical and Computer engineering, Qom university of technology, Qom, Iran.
AD - School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, 3122 Australia
AD - Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Y1 - 2018
PY - 2018
VL - 25
IS - 6
SP - 3628
EP - 3633
KW - Compressed sensing
KW - One bit measurements
KW - Bayesian hypothesis test
KW - ML estimator
DO - 10.24200/sci.2017.4374
N2 - 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.
UR - https://scientiairanica.sharif.edu/article_4374.html
L1 - https://scientiairanica.sharif.edu/article_4374_dd5ab11abdebdf11d3ff770a00e61405.pdf
ER -