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Covariance intersection fusion reduced-order Kalman estimator for multisensor descriptor systems

Covariance intersection fusion reduced-order Kalman estimato...

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第36届中国控制会议

作者： Chenjian Ran Yinfeng Dou Department of Automation Heilongjiang University

ISBN: (纸本)9781538629185

For the multisensor linear stochastic descriptor systems, the local reduced-order Kalman estimators and their estimation error variances are presented, based on the Kalman filtering theory and the singular value decomposition（SVD） method. Then, covariance intersection（CI） fusion reduced-order Kalman estimator and its estimation error variance are obtained fusing these local estimators. Compared with the state fusion method and the measurement fusion method, this CI fusion reduced-order Kalman estimation algorithm avoids computing cross-variances of local *** accuracy of CI fusion reduced-order Kalman estimator is higher than local estimators. A simulation example verifies the effectiveness of the presented algorithm.

关键词： multisensor descriptor systems CI fusion Reduced-order Kalman estimator

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Self-tuning WMF Kalman predictors for the multisensor descriptor systems and their convergence analysis

Self-tuning WMF Kalman predictors for the multisensor descri...

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第37届中国控制会议

作者： Yinfeng Dou Chenjian Ran Yuan Gao Yun Li Gang Hao Department of Automation Heilongjiang University

For the multisensor linear stochastic descriptor systems with uncorrelated noises, the self-tuning WMF prediction problem is solved, when the noise variances of the process noise and measurement noise are unknown. The consistent estimates of these unknown noise variances are obtained by applying the correlation method, and the weighted measurement fusion equation. Applying the singular value decomposition method and the classical Kalman filtering theory, substituting these consistent estimates of unknown noise variances into the optimal WMF reduced-order Kalman predictors yields the self-tuning reducedorder Kalman predictors. The convergence of these presented self-tunning WMF Kalman predictors is proven. An example of three-sensor descriptor systems verifies the effectiveness.

关键词： multisensor descriptor systems consistent estimates redcued-order method self-tuning Kalman predictors

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WMF full-order Kalman smoother for multisensor descriptor systems with correlated noises

WMF full-order Kalman smoother for multisensor Descriptor sy...

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第36届中国控制会议

作者： Chenjian Ran Yinfeng Dou Yuan Gao Department of Automation Heilongjiang University

ISBN: (纸本)9781538629185

For the multisensor descriptor systems with correlated noises,the WMF full-order Kalman smoother is ***,the centralized fusion descriptor system is *** this centralized fusion descriptor system is transformed to the descriptor system with uncorrelated ***,the weighted measurement fusion（WMF） descriptor system is ***,the WMF full-order Kalman and smoother and its smoothering error variance are presented,based on the maximum likelihood（ML） estimation and extended state method.A simulation example of a 3-sensor descriptor systems with common distribute noises verifies its effectiveness and the accuracy relation of the presented WMF full-order Kalman smoother.

关键词： multisensor descriptor systems correlated noises weighted measurement fusion full-order Kalman smoother

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