TY - GEN
T1 - Support agnostic Bayesian recovery of jointly sparse signals
AU - Masood, Mudassir
AU - Al-Naffouri, Tareq Y.
N1 - Publisher Copyright:
© 2014 EURASIP.
PY - 2014/11/10
Y1 - 2014/11/10
N2 - A matching pursuit method using a Bayesian approach is introduced for recovering a set of sparse signals with common support from a set of their measurements. This method performs Bayesian estimates of joint-sparse signals even when the distribution of active elements is not known. It utilizes only the a priori statistics of noise and the sparsity rate of the signal, which are estimated without user intervention. The method utilizes a greedy approach to determine the approximate MMSE estimate of the joint-sparse signals. Simulation results demonstrate the superiority of the proposed estimator.
AB - A matching pursuit method using a Bayesian approach is introduced for recovering a set of sparse signals with common support from a set of their measurements. This method performs Bayesian estimates of joint-sparse signals even when the distribution of active elements is not known. It utilizes only the a priori statistics of noise and the sparsity rate of the signal, which are estimated without user intervention. The method utilizes a greedy approach to determine the approximate MMSE estimate of the joint-sparse signals. Simulation results demonstrate the superiority of the proposed estimator.
UR - http://www.scopus.com/inward/record.url?scp=84911907444&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84911907444
T3 - European Signal Processing Conference
SP - 1741
EP - 1745
BT - 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
PB - European Signal Processing Conference, EUSIPCO
T2 - 22nd European Signal Processing Conference, EUSIPCO 2014
Y2 - 1 September 2014 through 5 September 2014
ER -