Data-adaptive principal component signal processing

Abstract

Principal component (eigenvalue-eigenvector) analysis is applied to processing of narrow band signals in noise. The amount of data available is assumed to be limited. Principal eigenvalues and eigenvectors of a sample correlation matrix are used to improve the signal to noise ratio (SNR) in the data and to increase the resolution capability of nonlinear least squares at low SNR and linear prediction based frequency estimation methods. Relation to Pronylike methods is explored. Performance of different methods is compared experimentally among themselves and to the Cramer-Rao (CR) bound.