Title :
Weighted covariance matching based square root LASSO
Author :
Owrang, Arash ; Jansson, Magnus
Author_Institution :
ACCESS Linnaeus Centre, KTH R. Inst. of Technol., Stockholm, Sweden
Abstract :
We propose a method for high dimensional sparse estimation in the multiple measurement vector case. The method is based on the covariance matching technique and with a sparse penalty along the ideas of the square-root LASSO (sr-LASSO). The method not only benefits from the strong characteristics of sr-LASSO (independence of the hyper-parameter selection from the noise variance), but also offers a performance near maximum likelihood. It performs close to the Cramer-Rao bound even at low signal to noise ratios and it is generalized to manage correlated noise. The only assumption in this matter is that the noise covariance matrix structure is known. The numerical simulation provided in an array processing application illustrates the potential of the method.
Keywords :
array signal processing; covariance matrices; maximum likelihood estimation; Cramer-Rao bound; array processing application; correlated noise management; high dimensional sparse estimation; maximum likelihood estimation; noise covariance matrix; signal to noise ratio; sr-LASSO; weighted covariance matching based square root LASSO; Face; Noise; correlated noise; covariance matching; multiple measurements; sparse estimation;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
DOI :
10.1109/ICASSP.2015.7178672