Title :
An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework
Author :
Lifan Zhao ; Guoan Bi ; Lu Wang ; Haijian Zhang
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
Abstract :
This letter considers the multiplicative perturbation problem in compressive sensing, which has become an increasingly important issue on obtaining robust performance for practical applications. The problem is formulated in a probabilistic model and an auto-calibration sparse Bayesian learning algorithm is proposed. In this algorithm, signal and perturbation are iteratively estimated to achieve sparsity by leveraging a variational Bayesian expectation maximization technique. Results from numerical experiments have demonstrated that the proposed algorithm has achieved improvements on the accuracy of signal reconstruction.
Keywords :
Bayes methods; calibration; compressed sensing; expectation-maximisation algorithm; learning (artificial intelligence); signal reconstruction; variational techniques; autocalibration algorithm improvement; compressive sensing; iterative estimation; multiplicative perturbation problem; probabilistic model; signal reconstruction; sparse Bayesian learning framework; variational expectation maximization technique; Bayes methods; Compressed sensing; Gaussian distribution; Noise; Numerical models; Signal processing algorithms; Sparse matrices; Auto-calibration; compressive sensing; multiplicative perturbation; sparse Bayesian framework;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2272462