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Robust Covariance Estimation for Data Fusion From Multiple Sensors [PDF]

IEEE Transactions on Instrumentation and Measurement, 2011
This paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics.
Antonios Tsourdos, Samuel B Lazarus
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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
openaire   +1 more source

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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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
openaire   +2 more sources