B15-1 Reduced Complexity High Performance Array Processing

Improve passive sonar systems by fielding adaptive beamforming algorithms that are computationally efficient, that operate effectively during rapid array maneuvers, and are able to track and remove interference from fast-moving surface traffic. OBJECTIVES Study a new approach to partially adaptive sonar signal processing and:-Demonstrate its ability to solve matched field source localization problems.-Compare its performance in acoustic waveguides to eigenvalue-based designs.-Determine when and why nested Wiener filters outperform eigenvalue-based designs. APPROACH This project is motivated by the signal processing needs of current and future Navy sonar arrays comprised of dozens or hundreds of sensors. Fully adaptive beamformers for such large arrays are impractical: they are too computationally demanding, and data adaptive implementations require too many independent, stationary samples. “Partially adaptive ” or “reduced complexity ” adaptive beamformers can be used to contend with these difficulties [4]-[7]. It is well known that decreasing adaptive degrees of freedom reduces data requirements, speeds adaptation in dynamic environments, and reduces computational overhead. The best-known design techniques are based upon principal components analysis of estimated covariance matrices. However, they are computationally costly because of the eigenvalue decomposition, an