NDLI: Self-tuning weighted measurement fusion Kalman filter and its convergence analysis

Content Provider	IEEE Xplore Digital Library
Author	Chenjian Ran Zili Deng
Copyright Year	2009
Description	Author affiliation: Department of Automation Heilongjiang University, Harbin150080, China (Chenjian Ran; Zili Deng)
Abstract	For the multisensor systems with unknown noise variances, using correlation method and least squares fusion criterion, information fusion noise variance estimators are presented by the average of local noise variance estimators, which have the consistence. Substituting the fused noise variance online estimators into the optimal Riccati equation and the optimal weighted measurement fusion Kalman filter, a self-tuning Riccati equation and a new self-tuning weighted measurement fusion Kalman filter are presented. In order to prove the convergence of the self-tuning Riccati equation, a dynamic variance error system analysis (DVSEA)method is presented, which converts the convergence problem to the stability problem of a time-varying Lyapunov equation. A stability decision criterion is presented for the Lyapunov equation. By the dynamic error system analysis (DESA) method and DVSEA method, it proves that the self-tuning weighted measurement fusion Kalman filter converges to the globally optimal weighted measurement fusion Kalman filter in a realization, so that it has asymptotic global optimality. A simulation example for target tracking system with 3-sensor shows its effectiveness.
Starting Page	1830
Ending Page	1835
File Size	712733
Page Count	6
File Format	PDF
ISBN	9781424438716
ISSN	01912216
DOI	10.1109/CDC.2009.5399610
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Weight measurement Convergence Riccati equations Noise measurement Error analysis Multisensor systems Correlation Least squares approximation Analysis of variance Stability analysis
Content Type	Text
Resource Type	Article

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