Multi-model information fusion Kalman filtering and white noise deconvolution

Authors:
Xiao-Jun Sun;Yuan Gao;Zi-Li Deng;Chuang Li;Jia-Wei Wang
Affiliations:
Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China;Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China;Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China;Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China;Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China
Venue:
Information Fusion
Year:
2010

Citing 7
Cited 6

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Brief paper: Optimal Kalman filtering fusion with cross-correlated sensor noises

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Multisensor optimal information fusion input white noise deconvolution estimators

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Technical Communique: Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor

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New approach to information fusion steady-state Kalman filtering

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Technical communique: Distributed optimal component fusion weighted by scalars for fixed-lag Kalman smoother

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Sequential covariance intersection fusion Kalman filter

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Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises

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Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

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The accuracy comparison of multisensor covariance intersection fuser and three weighting fusers

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Two-level robust sequential covariance intersection fusion Kalman predictors over clustering sensor networks with uncertain noise variances

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Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances

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Abstract

For the linear discrete time-varying stochastic control systems with multi-model and multisensor, using the Kalman filtering method, based on the Riccati equations and Lyapunov equations, according to three optimal fusion rules weighted by matrices, diagonal matrices, and scalars, three optimal weighted fusion Kalman estimators and white noise deconvolution estimators are presented in a unified framework, respectively. The corresponding steady-state local and fused estimators also are presented. The accuracy of the fuser with the matrix weights is higher than that of the fuser with scalar weights, and the accuracy of the fuser with diagonal matrix weights is between both of them. The accuracy of the fusers is higher than that of each local estimator. They can handle the fused filtering, smoothing and prediction problems. They can be applied to the information fusion filtering of the state and input white noises for the multisensor systems with the colored measurement noises. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are proposed. Two Monte Carlo simulation examples for an infrared target tracking system and a Bernoulli-Gaussian white noise deconvolution system show their effectiveness.