A Unified Approach to Optimal State Estimation for Stochastic Singular Systems

Zhang, Huanshui; Chai, Tianyou; Liu, Xiangjie

doi:10.1016/s0005-1098(98)00020-x

Cited by 33 publications

(24 citation statements)

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“…Otherwise determine the optimum scalar parameter by minimizing the function of (24) with the identifications (67) over the interval and set (27) Step Step 3: Update…”

Section: ) Robust Filtered Information Estimatementioning

confidence: 99%

Information Filtering and Array Algorithms for Descriptor Systems Subject to Parameter Uncertainties

Terra

Ishihara

Padoan

2007

IEEE Trans. Signal Process.

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Abstract-This paper presents information filters in Riccati recursions and in array algorithms for descriptor systems subject to parameters uncertainties. The filters are developed in filtered and predicted forms. The inversion of the state matrix is avoided in the new information recursive formulas. Therefore, it turns out clear that the invertibility of the state matrix, usually considered in the state-space information recursions, is not necessary. A numerical example is provided to illustrate the performance of the proposed robust array algorithms.Index Terms-Array algorithm, descriptor systems, information filter, Kalman filter.

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“…Otherwise determine the optimum scalar parameter by minimizing the function of (24) with the identifications (67) over the interval and set (27) Step Step 3: Update…”

Section: ) Robust Filtered Information Estimatementioning

confidence: 99%

Information Filtering and Array Algorithms for Descriptor Systems Subject to Parameter Uncertainties

Terra

Ishihara

Padoan

2007

IEEE Trans. Signal Process.

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show abstract

“…Its advantages are that it can reduce the computational burden and increase the input data rates significantly, and it can facilitate fault diagnosis and isolation. The limitation of the above descriptor state estimators [1][2][3][4][5][6][7][8][9] is that they only are suitable for a single sensor descriptor system. When a descriptor system is measured by multisensor, it is very important how to solve the information fusion state estimation problem.…”

Section: Introductionmentioning

confidence: 99%

“…For example, an application background is a maneuvering target tracking system with multisensor and unknown maneuvering acceleration [1]. The centralized fusion descriptor state estimators can be obtained by approaches as in [1][2][3][4][5][6][7][8][9] based on combining all local measurement equations as an augmented measurement equation. They are globally optimal, but their drawback is that a larger computational burden is required, because the high-dimensional matrix inverse is required to compute.…”

Section: Introductionmentioning

confidence: 99%

“…They are globally optimal, but their drawback is that a larger computational burden is required, because the high-dimensional matrix inverse is required to compute. The main difficulty of finding the distributed (weighting) fusion descriptor state estimators is to compute the crosscovariances among local estimation errors, which were not solved in [1][2][3][4][5][6][7][8][9]. This is an open problem.…”

Section: Introductionmentioning

confidence: 99%

“…They can handle the fused filtering, smoothing, and prediction problems in a unified framework, and they realize decoupled fusers for two subsystems. The limitation of [1][2][3][4][5][6][7][8][9] is overcomed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2008

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State estimation for multi‐channel stochastic singular systems with multiplicative noise

Chu

Gao

Guo

2010

Asian Journal of Control

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The state estimation problem for multi‐channel singular systems with multiplicative noise is considered based on singular value decomposition. First, two equivalent reduced order subsystems are obtained via the decomposition. Then, in order to solve the estimation problem, the subsystems are rewritten into a new form. It is noted that the measurement noise here becomes colored noise, which contains the dynamic noise, measurement noise, and multiplicative noise of the original system. In this situation, existing filtering methods cannot be directly applied, so a modified filtering method is given. The recursive algorithm for the state estimation is obtained by the filtering method. In addition, the estimation of dynamic noise is derived via the algorithm. A simulation example is given to show the effectiveness of the proposed algorithm. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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A Unified Approach to Optimal State Estimation for Stochastic Singular Systems

Cited by 33 publications

References 0 publications

Information Filtering and Array Algorithms for Descriptor Systems Subject to Parameter Uncertainties

Information Filtering and Array Algorithms for Descriptor Systems Subject to Parameter Uncertainties

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

State estimation for multi‐channel stochastic singular systems with multiplicative noise

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