Support agnostic Bayesian matching pursuit for block sparse signals
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2013-05Permanent link to this record
http://hdl.handle.net/10754/564708
Metadata
Show full item recordAbstract
A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.Citation
Masood, M., & Al-Naffouri, T. Y. (2013). Support agnostic Bayesian matching pursuit for block sparse signals. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. doi:10.1109/icassp.2013.6638540Conference/Event name
2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013ISBN
9781479903566ae974a485f413a2113503eed53cd6c53
10.1109/ICASSP.2013.6638540