Results 161 to 170 of about 192,768 (193)

Estimation of Block-Toeplitz Covariance Matrices

1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990., 2005
We consider the problem of estimating structured covarimws using maximum-likelihood methodology. The structured covariance matrices of interest here are block-Toeplitz matrices and Toeplitz-block-Toeplitz matrices. These arise naturally in wideband array processing, and in narrowband array processing with rectangular arrays.
D.R. Fuhrmann, T.A. Barton
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Robust estimation of covariance matrices

IEEE Transactions on Automatic Control, 1990
Data manipulations which increase the robustness and accuracy of estimators of covariance parameters by using the innovations correlation approach are considered. The procedures are especially useful for improving estimates of process-noise covariance parameters for slowly varying systems when measurement noise is large.
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Stable estimators of inverse covariance matrices

Journal of Mathematical Sciences, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the estimation of structured covariance matrices

Automatica, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
ZORZI, MATTIA, FERRANTE, AUGUSTO
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Computationally efficient maximum-likelihood estimation of structured covariance matrices

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 1998
Summary: By invoking the extended invariance principle (EXIP), we present a computationally efficient method that provides asymptotic (for large samples) maximum likelihood (AML) estimation for structured covariance matrices and will be referred to as the AML algorithm.
Li, Hongbin, Stoica, Petre, Li, Jian
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Positive definite estimators of large covariance matrices

Biometrika, 2012
Using convex optimization, we construct a sparse estimator of the covariance matrix that is positive definite and performs well in high-dimensional settings. A lasso-type penalty is used to encourage sparsity and a logarithmic barrier function is used to enforce positive definiteness.
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Estimation of graph Laplacian and covariance matrices

2019
Research in recent years has been dominated by bigger and more complex types of data. Multimedia signals (images, video, holograms) and non traditional signals arising in sensor and social networks have become pervasive while increasing in complexity and quantity.
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Estimation Of Structured Covariance Matrices For Tomosar Focusing

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023
Martin del Campo Becerra, Gustavo Daniel, Torres García, Eduardo, Nannini, Matteo, Reigber, Andreas, Torres Román, Deni Librado   +4 more
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Estimation of High-Dimensional Inverse Covariance Matrices

2000
In this Chapter we construct stable asymptotically unimprovable estimators for increasing inverse covariance matrices.
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Bayesian Estimation of Block Covariance Matrices

SSRN Electronic Journal, 2022
Drew Creal, Jaeho Kim
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