Eigenfilter approaches to adaptive array processing

Abstract

High-resolution methods of spectral estimation have recently found application in the processing of data received by spatially distributed arrays of sensors. These techniques have been used for estimation of the directional power illuminating the array and have proved useful as a first step in the analysis and classification of possible targets. Several desirable qualities are offered by these spatial spectral analysis techniques: high resolution for short arrays, low sidelobe levels, ability to work with arbitrary array geometries, and tolerance to correlated (multipath) targets being but a few. Spectral estimators based on certain eigenvectors of the data covariance matrix have these properties. The paper discusses the use of filters based on these eigenvectors (called eigenfilters) in adaptive array processing, and two different estimation algorithms are compared. The first algorithm involves a simple gradient descent approach, while the second utilises a decomposition of the array data by a spatial filter which leads to a triangular structured lattice filter. Comparisons are made between these algorithms and with other conventional high-resolution spectral estimators.