2016
Subspace fitting via sparse representation of signal covariance for DOA estimation
ICASSP 2016accepted
Based on the orthogonality between the signal subspace and the noise subspace, we propose a sparse recovery method for the direction of arrival (DOA) estimation. With the assumption of uncorrelated sources, signal covariance matrix fitting is achieved by embedding the MUSIC-like weights into a quadr…