Reduced-order steady-state descriptor Kalman fuser weighted by block-diagonal matrices

Deng, Zili; Gao, Yuan; Tao, Guili

doi:10.1016/j.inffus.2006.10.010

Cited by 41 publications

(21 citation statements)

References 15 publications

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“…D i (q −1 ) and Q εi can be obtained by Gevers-Wouters [9]'s iteration algorithm. From (7) and (8) we obtain the ARMA innovation model…”

Section: Local Descriptor Wiener State Estimatorsmentioning

confidence: 99%

“…Recently, the optimal fusion rule weighted by diagonal matrices has been presented [6], which realizes a decoupled fusion estimation for state components. Using the singular value decomposition, transforming the descriptor system into two reducedorder non-descriptor subsystems, the distributed descriptor Kalman fuser has been presented [7,8]. In this paper, for discrete linear stochastic descriptor systems with multisensor, using the modern time series analysis method [4,5], based on the autoregressive moving average (ARMA) innovation model and white noise estimators [4,5], using the optimal fusion rule weighted by diagonal matrices, a decoupled distributed descriptor Wiener state fuser is presented, which avoids the decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Decoupled Wiener state fuser for descriptor systems

Ran

Deng

2008

J. Control Theory Appl.

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By the modem time series analysis method, based on the autoregressive moving average (ARMA) innovation models and white noise estimation theory, using the optimal fusion rule weighted by diagonal matrices, a distributed descriptor Wiener state fuser is presented by weighting the local Wiener state estimators for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented based on cross-covariances among the local innovation processes, input white noise, and measurement white noises. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness and correctness.

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“…D i (q −1 ) and Q εi can be obtained by Gevers-Wouters [9]'s iteration algorithm. From (7) and (8) we obtain the ARMA innovation model…”

Section: Local Descriptor Wiener State Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoupled Wiener state fuser for descriptor systems

Ran

Deng

2008

J. Control Theory Appl.

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“…For the single sensor descriptor systems, there exist different approaches in the literature [4–8] to deal with their estimation problems. They include reduced‐order estimation methods [9, 10] and the full‐order estimation methods [11–13]. Applying the reduced‐order methods [9, 10, 14], a descriptor system can be transformed into two reduced‐order normal systems by a singular value decomposition method, which can reduce computational burden, while the assumption condition of this algorithm is too rigorous.…”

Section: Introductionmentioning

confidence: 99%

Self‐tuning full‐order WMF Kalman filter for multisensor descriptor systems

Dou

Sun

Ran

2017

IET Control Theory & Applications

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“…The so‐called linearly correlated noises mean that the measurement noise is a linear function of the process noise, which are encountered frequently in practical applications. For example, using the singular value decomposition, the descriptor system can be transformed into two reduced‐order non‐singular coupled subsystems; where in the first subsystem, the measurement noise is linearly correlated with the process noise . For the system with colored measurement noise, using the measurement differencing transformation, the system can be converted into an equivalent system with linearly correlated noises .…”

Section: Introductionmentioning

confidence: 99%

Robust weighted fusion Kalman estimators for multisensor systems with multiplicative noises and uncertain‐covariances linearly correlated white noises

Liu

Wang

Deng

2016

Intl J Robust & Nonlinear

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Summary This paper addresses the design of robust weighted fusion Kalman estimators for a class of uncertain multisensor systems with linearly correlated white noises. The uncertainties of the systems include the same multiplicative noises perturbations both on the systems state and measurement output and the uncertain noise variances. The measurement noises and process noise are linearly correlated. By introducing two fictitious noises, the system under consideration is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case systems with the conservative upper bounds of the noise variances, the four robust weighted fusion time‐varying Kalman estimators are presented in a unified framework, which include three robust weighted state fusion estimators with matrix weights, diagonal matrix weights, scalar weights, and a modified robust covariance intersection fusion estimator. The robustness of the designed fusion estimators is proved by using the Lyapunov equation approach such that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The accuracy relations among the robust local and fused time‐varying Kalman estimators are proved. The corresponding robust local and fused steady‐state Kalman estimators are also presented, a simulation example with application to signal processing to show the effectiveness and correctness of the proposed results. Copyright © 2016 John Wiley & Sons, Ltd.

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Reduced-order steady-state descriptor Kalman fuser weighted by block-diagonal matrices

Cited by 41 publications

References 15 publications

Decoupled Wiener state fuser for descriptor systems

Decoupled Wiener state fuser for descriptor systems

Self‐tuning full‐order WMF Kalman filter for multisensor descriptor systems

Robust weighted fusion Kalman estimators for multisensor systems with multiplicative noises and uncertain‐covariances linearly correlated white noises

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