Results 291 to 300 of about 192,869 (306)

Estimation of hyperspectral covariance matrices

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
Estimation of covariance matrices is a fundamental step in hyperspectral remote sensing where most detection algorithms make use of the covariance matrix in whitening procedures. We present a simple method to improve the estimation of the eigenvalues of a sample covariance matrix. With the improved eigenvalues we construct an improved covariance matrix.
Avishai Ben-David, Charles E. Davidson
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Robust estimation of structured covariance matrices

IEEE Transactions on Signal Processing, 1993
Summary: In the context of the narrow-band array processing problem, we develop robust methods to accurately estimate the spatial correlation matrix using a priori information about the matrix structure. For Gaussian processes, structured estimates previously have been developed which find the maximum likelihood covariance matrix estimate subject to ...
Williams, Douglas B., Johnson, Don H.
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Estimation of Space-Varying Covariance Matrices

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
This paper considers the representation of human trajectories in video signals. These trajectories are modeled by switched dynamical models, based on motion fields that drive the pedestrian during consecutive time intervals. This paper addresses the estimation of uncertainty in trajectory generation by using space-varying covariance matrices estimated ...
Catarina Barata, Joao M. Lemos, Jorge S. Marques   +2 more
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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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