Direction finding based on spatial time-frequency distribution matrices

Spatial time-frequency distributions (STFDs) have been recently introduced as the natural means to deal with source signals that are localizable in th e time-frequency domain. It has been shown that improved estimates of the s ignal and noise subspaces are achieved by constructing the subspaces from t he time-frequency signatures of the signal arrivals rather than from the da ta covariance matrices, which are commonly used in conventional subspace es timation methods. This paper discusses the application of STFD to high-reso lution direction finding. We focus on both the role and the effect of cross terms in angle estimation when multiple time-frequency points are incorpora ted. Simulation examples are presented to compare the performance of joint block-diagonalization and time-frequency averaging techniques for incorpora ting multiple autoterm and crossterm points in subspace estimation. (C) 200 0 Academic Press.