Derived PDF of maximum likelihood signal estimator which employs an estimated noise covariance

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

A probability density function (pdf) for the maximum likelihood (ML) signal vector estimator is derived when the estimator relies on a noise sample covariance matrix (SCM) for evaluation. By using a complex Wishart probabilistic model for the distribution of the SCM, it is shown that the pdf of the adaptive ML (AML) signal estimator (alias the SCM based minimum variance distortionless response (MVDR) beamformer output and, more generally, the SCM based linearly constrained minimum variance (LCMV) beamformer output) is, in general, the confluent hypergeometric function of a complex matrix argument known as Kummer's function. The AML signal estimator remains unbiased but only asymptotically efficient; moreover, the AML signal estimator converges in distribution to the ML signal estimator (known noise covariance). When the sample size of the estimated noise covariance matrix is fixed, it is demonstrated that there exists a dynamic tradeoff between signal-to-noise ratio (SNR) and noise adaptivity as the dimensionality of the array data (number of adaptive degrees of freedom) is varied, suggesting the existence of an optimal array data dimension that will yield the best performance.

Original languageEnglish (US)
Pages (from-to)305-315
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume44
Issue number2
DOIs
StatePublished - Dec 1 1996
Externally publishedYes

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Covariance matrix
Maximum likelihood
Probability density function
Signal to noise ratio

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Derived PDF of maximum likelihood signal estimator which employs an estimated noise covariance. / Richmond, Christ.

In: IEEE Transactions on Signal Processing, Vol. 44, No. 2, 01.12.1996, p. 305-315.

Research output: Contribution to journalArticle

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