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Application of Maximum Likelihood Estimation of Persymmetric Covariance Matrices to Adaptive Processing | IEEE Journals & Magazine | IEEE Xplore

Application of Maximum Likelihood Estimation of Persymmetric Covariance Matrices to Adaptive Processing


Abstract:

The optimum weights for an adaptive processor are determined by solving a particular matrix equation. When, as is usually true in practice, the covariance matrix is unkno...Show More

Abstract:

The optimum weights for an adaptive processor are determined by solving a particular matrix equation. When, as is usually true in practice, the covariance matrix is unknown, a matrix estimator is required. Estimating the matrix can be computationally burden some. Methods of decreasing the computational burden by exploiting persymmetric symmetries are discussed. It is shown that the number of independent vector measurements required for the estimator can be decreased by up to a factor of two.
Published in: IEEE Transactions on Aerospace and Electronic Systems ( Volume: AES-16, Issue: 1, January 1980)
Page(s): 124 - 127
Date of Publication: 31 January 1980

ISSN Information:


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