Random Matrix Theory Analysis of Adaptive Beamformers
Abstract
Adaptive beamformers are sensor array signal processing algorithms which seek to suppress loud interferers in order to improve both detection range and accuracy in target bearing estimation. While adaptive algorithms have many advantages over deterministic beamformers, it is difficult to predict their performance in practical scenarios. Large arrays operating in non-stationary environments offer a particular challenge for performance prediction as these systems rarely obtain sufficient snapshots to estimate the signal statistics accurately. Recent mathematical developments in random matrix theory provide new tools for analyzing the statistical behavior of sample covariance matrices, which are the heart of any adaptive beamformer. The proposed research seeks to develop novel adaptive beamformers for passive sonar using random matrix theory and to characterize the performance of existing passive adaptive beamformers using the latest results from random matrix theory research.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 26, 2018
- Source ID
- N000141812669
Entities
People
- Kathleen E Wage
Organizations
- George Mason University
- Office of Naval Research
- United States Navy