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Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo
Author(s) -
Matisko Peter,
Havlena Vladimír
Publication year - 2013
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2369
Subject(s) - covariance , kalman filter , monte carlo method , extended kalman filter , covariance intersection , noise (video) , computer science , covariance matrix , algorithm , mathematics , control theory (sociology) , statistics , artificial intelligence , control (management) , image (mathematics)
SUMMARY Linear time‐invariant systems play significant role in the control field. A number of methods have been published for identification of the deterministic part of a process. However, identification of the stochastic part has had much less attention. This paper deals with estimation of covariance matrices of the noise entering a linear system. The process and measurement noise covariance matrices are tuning parameters of the Kalman filter, and they affect the quality of the state estimation. The noise covariance matrices are generally not known, and their estimation from the measured data is a challenging task. This paper introduces a method for estimation of the noise covariance matrices using Bayesian approach along with Monte Carlo numerical methods. Performance of the approach is tested on various systems and noise properties. The second part of the paper compares Monte Carlo approach with the recently published methods. The speed of convergence is compared with the Cramér–Rao bounds. Copyright © 2012 John Wiley & Sons, Ltd.