Maximum-Likelihood Direction Finding of Wide-Band Sources

Miriam A. Doron, Anthony J. Weiss, Hagit Messer

Research output: Contribution to journalArticlepeer-review


In this correspondence, we consider the maximum-likelihood (ML) direction-finding problem for wide-band sources. We examine the separable Gaussian and deterministic ML estimators, for several cases of a priori noise statistics. We derive general conditions under which the deterministic ML estimate is also an extremum of the Gaussian likelihood function. We also analyze the asymptotic performance of the wide-band ML estimators. We show that it is not affected by a priori knowledge of the noise spectrum, and formulate conditions under which the asymptotic performance of the deterministic and the Gaussian ML estimators are equal.

Original languageEnglish
Pages (from-to)411
Number of pages1
JournalIEEE Transactions on Signal Processing
Issue number1
StatePublished - Jan 1993
Externally publishedYes


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