Superresolution by Structured Matrix Approximation
Date of Original Version
High-resolution bearing estimation with arrays of limited aperture is often accomplished using the eigenvalue-eigenvector decomposition of the spatial correlation matrix of the received data. 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 XM in the least square sense. The rank as well as the partial structure of the space spanned by the columns of [formula Omitted]XM are prespecified. After XM is computed, the bearings of the sources and, consequently, the spatial correlation of the source signals can be estimated. The performance of the proposed technique is then compared with two existing methods by simulation. The comparison has been done in terms of bias, mean-squared error, failure rates, and confidence intervals for the mean and the variance estimates for the 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. © 1988 IEEE
IEEE Transactions on Antennas and Propagation
Kumaresan, Ramdas, and Arnab K. Shaw. "Superresolution by Structured Matrix Approximation." IEEE Transactions on Antennas and Propagation 36, 1 (1988): 34-44. doi:10.1109/8.1072.