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research
CELLO: A fast algorithm for Covariance Estimation
Authors
Abraham Galton Bachrach
Adam P. Bry
+3 more
Jonathan S. Kelly
Nicholas Roy
William R Vega-Brown
Publication date
10 April 2018
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
We present CELLO (Covariance Estimation and Learning through Likelihood Optimization), an algorithm for predicting the covariances of measurements based on any available informative features. This algorithm is intended to improve the accuracy and reliability of on-line state estimation by providing a principled way to extend the conventional fixed-covariance Gaussian measurement model. We show that in experiments, CELLO learns to predict measurement covariances that agree with empirical covariances obtained by manually annotating sensor regimes. We also show that using the learned covariances during filtering provides substantial quantitative improvement to the overall state estimate. © 2013 IEEE.United States. National Aeronautics and Space AdministrationSiemens Corporate ResearchUnited States. Office of Naval Research. Multidisciplinary University Research InitiativeMicro Autonomous Consortium Systems and Technolog
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Last time updated on 09/05/2018