Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation
This work addresses signal reconstruction challenges in compressed sensing for applications like sensor networks, but it is incremental as it builds on existing methods.
The paper tackles noisy sparse recovery in one-bit compressed sensing with sensing matrix perturbation by proposing a low-computational Bayesian algorithm called BHT-MLE, which improves reconstruction accuracy over an ML estimator at lower computational cost.
This letter 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 BHT-MLE algorithm offers more reconstruction accuracy than that of an ML estimator (MLE) at a low computational cost.