Title
The Role of Subspace Estimation in Array Signal Processing
Document Type
Conference Proceeding
Date of Original Version
11-1-2019
Abstract
Subspace-based algorithms for array signal processing typically begin with an eigenvalue decomposition of a sample covariance matrix. The eigenvectors are partitioned into two sets to get bases for signal and noise subspaces. However, the eigenvector subspace estimates are not the most accurate estimates obtainable from the data. Accuracy is defined here in terms of an intrinsic Cramer-Rao (CR) bound. A closed-form (non-iterative) algorithm that achieves the CR bound on subspace accuracy is derived, and examples are given for the applications of adaptive beamforming with a line array and DOA estimation with a planar array. The new algorithm requires far fewer snapshots (e.g. 10 to 100 times fewer) than the typical eigenvector approach to achieve a given level of performance.
Publication Title, e.g., Journal
Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume
2019-November
Citation/Publisher Attribution
Vaccaro, Richard J.. "The Role of Subspace Estimation in Array Signal Processing." Conference Record - Asilomar Conference on Signals, Systems and Computers 2019-November, (2019): 1566-1572. doi: 10.1109/IEEECONF44664.2019.9048994.