COVARIANCE MATCHING ESTIMATION TECHNIQUES FOR ARRAY SIGNAL-PROCESSINGAPPLICATIONS

A class of covariance matching estimation techniques (COMET) has recen tly attracted interest in the signal processing community. These techn iques have their roots in the statistical literature where they are so metimes referred to as generalized least squares methods. Covariance m atching is an alternative to maximum likelihood estimation, providing the same large sample properties often at a lower computational cost. Herein, we present a general framework. for covariance matching techni ques and show that they are well suited to solve several problems aris ing in array signal processing. A straightforward derivation of the CO MET criterion from first principles is presented, which also establish es the large sample properties of the estimator. Closed form compact e xpressions for the asymptotic covariance of the estimates of the param eters of interest are also derived. Some detection schemes are reviewe d and two COMET-based detection schemes are proposed. The main part of the paper treats three applications where the COMET approach proves i nteresting. First, we consider the localization of underwater sources using a hydro-acoustic array. The background noise is often spatially correlated in such an application and this must be taken into account in the estimation procedure. Second, the problem of channel estimation in wireless communications is treated. In digital communications, an estimate of the channel is often required to perform accurate demodula tion as well as spatially selective transmission. Finally, a radar det ection problem is formulated and the proposed detection schemes are ev aluated. (C) 1998 Academic Press