Superresolution by structured matrix approximation

TLDR: In this paper, the bearing estimation problem is formulated as a matrix-approximation problem and the columns of a matrix X are formed by the snapshot vectors from an N-element array.

Abstract: The bearing estimation problem is formulated as a matrix-approximation problem. The columns of a matrix X are formed by the snapshot vectors from an N-element array. The matrix X is then approximated by a matrix in the least-square sense. The rank as well as the partial structure of the space spanned by the columns of the approximated X matrix are prespecified. After the approximated X matrix is computed, the bearings of the sources and, consequently, the spatial correlation of the source signals are estimated. The performance of the proposed technique is compared with two existing methods using simulation. The comparison is made in terms of bias, mean-squared error, failure rates, and confidence intervals for the mean and the variance estimates for all three methods at different signal-to-noise ratios. When the sources are moving slowly and the number of snapshot vectors available for processing is large, a simple online adaptive algorithm is suggested. >