Results 271 to 280 of about 134,557 (305)

Magnetic Heterodyne Target Proximal Distance Estimate Using Extended N-th-Pole Magnetic Dipole Model via Iterative Extended Kalman Filter. [PDF]

Sensors (Basel)
Miao X, Li Y, Jiang Z, Ma S, Zhang H, Liu P, Dai K.   +6 more
europepmc   +1 more source

Structured Robust Covariance Estimation

Foundations and Trends® in Signal Processing, 2015
We consider robust covariance estimation with an emphasis on Tyler’s M-estimator. This method provides accurate inference of an unknown covariance in non-standard settings, including heavy-tailed distributions and outlier contaminated scenarios. We begin with a survey of the estimator and its various derivations in the classical unconstrained settings.
Wiesel, Ami, Zhang, Teng
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Threshold Selection for Covariance Estimation

Biometrics, 2019
Abstract Thresholding is a regularization method commonly used for covariance estimation, which provides consistent estimators if the population covariance satisfies certain sparsity condition (Bickel and Levina, 2008a; Cai and Liu, 2011). However, the performance of the thresholding estimators heavily depends on the threshold level.
Yumou Qiu, Janaka S. S. Liyanage
openaire   +3 more sources

On process noise covariance estimation

2017 25th Mediterranean Conference on Control and Automation (MED), 2017
This paper proposes a method for estimating the process noise covariance matrix, using multiple Kalman filters. The basic idea is to employ the difference between the expected prediction error covariance, calculated in the Kalman filters, and the measured prediction error covariance.
Hoai-Nam Nguyen, Fabrice Guillemin
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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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Calibrated Estimators of the Population Covariance

Acta Applicandae Mathematicae, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Plikusas, Aleksandras, Pumputis, Dalius
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The decentralized estimation of the sample covariance

2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008
In this paper we consider the problem of estimating the eigenvectors of the sample covariance matrix of decentralized measurements in a distributed fashion. The need for a distributed scheme is motivated by the many moment based methods that resort to the covariance of the data to extract information from the measurements.
Anna Scaglione, Roberto Pagliari, Hamid Krim   +2 more
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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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A Nonparametric Prewhitened Covariance Estimator

Journal of Time Series Analysis, 2002
This paper proposes a new nonparametric spectral density estimator for time series models with general autocorrelation. The conventional nonparametric estimator that uses a positive kernel has mean squared error no better than n−4/5. We show that the best implementation of our estimator has mean squared error of order n−8/9, provided there is ...
Xiao, Zhijie, Linton, Oliver
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