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Robust weighted fusion Kalman estimators for systems with multiplicative noises, missing measurements and uncertain-variance linearly correlated white noises

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AEROSPACE SCIENCE AND TECHNOLOGY 2017年 68卷 331-344页

作者： Wang, Xuemei Liu, Wenqiang Deng, Zili Heilongjiang Univ Elect Engn Coll Harbin 150080 Heilongjiang Peoples R China Heilongjiang Coll Technol & Business Harbin 150025 Heilongjiang Peoples R China

For linear discrete time-varying and time-invariant multisensor uncertain systems with multiplicative noises, missing measurements and uncertain-variance linearly correlated white noises, by introducing the fictitious noises to compensate the stochastic uncertainties, the system under consideration can be converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variances, the four robust weighted state fusion time-varying and steady-state Kalman estimators (predictor, filter, smoother) are presented respectively. They include the three fusers weighted respectively by matrices, scalar and diagonal matrices and a new modified covariance intersection (CI) fuser. They are designed in a unified framework, such that the filters and smoothers are designed based on the predictors. By the Lyapunov equation approach, their robustness is proved in the sense that for all admissible uncertainties, their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. The convergence in a realization between the robust fused time-varying and steady-state Kalman estimators for the time-varying and time-invariant systems are proved by the dynamic error system analysis (DESA) method. Their accuracy relations are also proved. A simulation example applied to uninterruptible power system (UPS) shows the effectiveness of the proposed results. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Weighted fusion Minimax robust Kalman filtering Multiplicative noise Missing measurement Uncertain noise variance convergence in a realization

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Self-tuning decoupled information fusion Wiener state component filters and their convergence

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AUTOMATICA 2008年第3期44卷 685-695页

作者： Deng, Zi-Li Gao, Yuan Li, Chun-Bo Hao, Gang Heilongjiang Univ Dept Automat Harbin Peoples R China

For the multisensor systems with unknown noise variances, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances are obtained, and under linear minimum variance optimal information fusion criterion weighted by scalars for state components, a class of self-tuning decoupled fusion Wiener filters is presented. It realizes the self-tuning decoupled local Wiener filters and self-tuning decoupled fused Wiener filters for the state components. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. The dynamic error system analysis (DESA) method is presented, by which the problem of convergence in a realization for self-tuning fusers is transformed into the stability problems of non-homogeneous difference equations, and the decision criterions of the stability are also presented. It is strictly proved that if the parameter estimation of the MA innovation models is consistent and if the measurement process is bounded in a realization or with probability one, then the self-tuning fusers will converge to the optimal fusers in a realization or with probability one, so that they have the asymptotic optimality. They can deal with the systems with the non-stationary or Gaussian measurement processes. They can reduce the computational burden, and are suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows their effectiveness. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： multisensor information fusion decoupled fusion self-tuning fusion noise variance estimation convergence in a realization Wiener state filter MA innovation model stability dynamic error system analysis method (DESA) modern time series analysis method

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Robust Kalman estimators for systems with multiplicative and uncertain-variance linearly correlated additive white noises

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AEROSPACE SCIENCE AND TECHNOLOGY 2018年 72卷 230-247页

作者： Liu, Wenqiang Wang, Xuemei Deng, Zili Heilongjiang Univ Elect Engn Coll Dept Automat Harbin 150080 Heilongjiang Peoples R China Heilongjiang Univ Sci & Technol Comp & Informat Engn Coll Harbin 150022 Heilongjiang Peoples R China

For linear discrete-time stochastic systems with mixed uncertainties including multiplicative and uncertain-variance linearly correlated additive white noises, this paper addresses the problem of designing robust Kalman estimators. By the fictitious noise-based Lyapunov equation approach, the system under consideration is converted into one with only uncertain noise variances. Based on the worst-case system with conservative upper bounds of actual noise variances, the minimax robust time varying Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Their robustness is proved in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The corresponding robust steady-state Kalman estimators are also presented. An innovation test rule and a search technique of selecting the less-conservative upper bounds of actual noise variances are presented, by which the two classes of guaranteed robust accuracy Kalman estimators are presented. One class is robust Kalman estimators with improved robust accuracy, the other class is ones with the prescribed robust accuracy index. Three modes of convergence in a realization among the time-varying and steady-state robust Kalman estimators for the time-varying and time-invariant systems are presented and proved by the dynamic error system analysis (DESA) method. Two simulation examples applied to autoregressive (AR) signal processing and uninterruptible power system (UPS) show the effectiveness of the proposed results. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Minimax robust Kalman estimators Multiplicative noises Uncertain noise variances Guaranteed accuracy Fictitious noise-based Lyapunov equation approach convergence in a realization

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Optimal and self-tuning weighted measurement fusion Wiener filter for the multisensor multichannel ARMA signals

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SIGNAL PROCESSING 2009年第5期89卷 738-752页

作者： Sun, Xiao-Jun Deng, Zi-Li Heilongjiang Univ Dept Automat Harbin 150080 Peoples R China

For the multisensor multichannel autoregressive moving average (ARMA) signals with white measurement noises, using the modern time series analysis method, based on the ARMA innovation models, white noise estimators, and measurement predictors, an optimal weighted measurement fusion Wiener filter is presented by the weighted least squares (WLS) method. It can handle the fused filtering, smoothing and prediction problems in a unified framework. When the noise variances and model parameters are unknown, based on the on-line identification of the local and fused ARMA innovation models, a self-tuning weighted measurement fusion Wiener filter is presented. By the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fuser converges to the optimal fuser in a realization, so that it has the asymptotic optimality. Compared with the globally optimal centralized fusion time-varying Kalman filter, the proposed optimal and self-tuning Wiener fusers;have the asymptotic global optimality, whose accuracies are higher than these of the optimal and self-tuning distributed Wiener fusers and local Wiener filters, respectively. A simulation example shows their effectiveness. (c) 2008 Elsevier B.V. All rights reserved.

关键词： Multisensor information fusion Weighted measurement fusion Multichannel ARMA signals Self-tuning Wiener filter convergence in a realization Asymptotic global optimality

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Self-tuning decoupled fusion Kalman filter based on the Riccati equation

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Frontiers of Electrical and Electronic Engineering in China 2008年第4期3卷 459-464页

作者： Xiaojun SUN Peng ZHANG Zili DENG Department of Automation Heilongjiang UniversityHarbin 150080China

An online noise variance estimator for multi-sensor systems with unknown noise variances is proposed by using the correlation *** on the Riccati equa-tion and optimal fusion rule weighted by scalars for state components,a self-tuning component decoupled informa-tion fusion Kalman filter is *** is proved that the filter converges to the optimal fusion Kalman filter in a realization by dynamic error system analysis method,so that it has asymptotic *** effectiveness is demon-strated by simulation for a tracking system with 3 sensors.

关键词： multi-sensor information fusion decoupled fusion self-tuning fuser Kalman filter convergence in a realization

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Self-tuning Measurement Fusion Kalman Filter with Correlated Measurement Noises and Its convergence

Self-tuning Measurement Fusion Kalman Filter with Correlated...

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27th Chinese Control Conference

作者： Gao Yuan Deng Zili Heilongjiang Univ Dept Automat Harbin 150080 Peoples R China

ISBN: (纸本)9787900719706

For the multisensor system with unknown noise statistics and correlated measurement noises, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances are obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented. Based on the stability of the dynamic error system and the concept of the convergence in a realization, it is strictly proved that the proposed self-tuning filter convergences to the steady-state optimal Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. Compared with the centralized self-tuning Kalman filter, it can reduce the computational burden, and is suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows its effectiveness.

关键词： Multisensor measurement fusion Self-tuning Kalman filter convergence in a realization Correlation method

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Self-tuning weighted measurement fusion Kalman smoother

Self-tuning weighted measurement fusion Kalman smoother

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IEEE International Conference on Control and Automation

作者： Gao, Yuan Jia, Wen-Jing Sun, Xiao-Jun Deng, Zi-Li Heilongjiang Univ Dept Automat Harbin Peoples R China

ISBN: (纸本)9781424408177

For the multisensor system with unknown noise statistics and with identical measurement matrices, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variance matrices are obtained, further, a self-tuning weighted measurement fusion Kalman smoother is presented. Based on the stability of the dynamic error system, a new convergence analysis tool is presented for self-tuning fuser. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is strictly proved that the proposed self-tuning fuser converges to the optimal Kalman fuser in a realization or with probability one, so that it has asymptotic global optimality. Compared with centralized self-tuning Kalman fuser, it can reduce the computational burden, and is suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows its effectiveness.

关键词： multisensor information fusion measurement fusion self-tuning Kalman smoother correlation method convergence in a realization

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SELF-TUNING MEASUREMENT FUSION KALMAN FILTER WITH CORRELATED MEASUREMENT NOISES

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Journal of Electronics(China) 2009年第5期26卷 614-622页

作者： Gao Yuan Ran Chenjian Deng Zili Department of Automation Heilongjiang University Harbin 150080 China

For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.

关键词： Correlation function method Multisensor measurement fusion Self-tuning Kalman filter convergence in a realization

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Self-tuning Measurement Fusion Filter and Its convergence Analysis

Self-tuning Measurement Fusion Filter and Its Convergence An...

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7th World Congress on Intelligent Control and Automation

作者： Gao, Yuan Deng, Zili Heilongjiang Univ Dept Automat Harbin 150080 Heilongjiang Pr Peoples R China

ISBN: (纸本)9781424421138

For the multisensor multi-channel autoregressive moving average (ARMA) signals with unknown parameters and noise variances, using the modern time series analysis method, based on the on-line identification of the local ARMA innovation models and fused moving average (MA) innovation model, a class of self-tuning weighted measurement fusion filter and smoother are presented. By using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning signal fusers converge to the optimal signal fusers in a realization. They can reduce the computational burden, and have asymptotic global optimality. A simulation example shows its effectiveness.

关键词： ARMA signal parameter and noise variance estimation self-tuning filter weighted measurement fusion convergence in a realization

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Self-tuning Reduced Dimension Measurement Fusion Kalman Filter

Self-tuning Reduced Dimension Measurement Fusion Kalman Filt...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Gao, Yuan Deng, Zili Heilongjiang Univ Dept Automat Harbin 150080 Heilongjiang Pr Peoples R China

ISBN: (纸本)9781424467129

For the multisensor systems with correlated measurement noises, different measurement matrices and unknown noise variances, based on the autoregressive moving average (ARMA) model and the reduced dimension measurement fusion algorithm, using the correlated method, a self-tuning reduced dimension measurement fusion Kalman filter is obtained, and its convergence in a realization is proved by the dynamic error system analysis (DESA) method. A simulation example shows its effectiveness.

关键词： Reduced dimension measurement fusion self-tuning filter dynamic error system analysis (DESA) method convergence in a realization

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