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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

Self-tuning weighted measurement fusion Kalman filter and its convergence

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控制理论与应用（英文版） 2010年第4期8卷 435-440页

作者： chenjian ran,zili deng (Department of Automation,Heilongjiang University,Harbin Heilongjiang 150080,China) Department of Automation Heilongjiang University Harbin Heilongjiang China

For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion *** these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is *** the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a ***,the self-tuning weighted measurement fusion Kalman filter has asymptotic global *** simulation example for a 4-sensor target tracking system verifies its effectiveness.

关键词： Multisensor weighted measurement fusion Fused parameter estimator Fused noise variance estimator Self-tuning fusion Kalman filter Asymptotic global optimality Convergence

Self-tuning Measurement Fusion Kalman Filter for Multisensor Systems with Companion Form and Common Disturbance Noise

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Self-tuning Measurement Fusion Kalman Filter for Multisensor...

第二十九届中国控制会议

作者： ran chenjian,deng zili Department of Automation,Heilongjiang University,Harbin 150080,P.R.China

For the multisensor systems with companion form and common disturbance noise,when the model parameters and noise variances are all unknown,using recursive extended least squares（RELS）algorithm,the local and fused estimators of part model parameters are *** then,the information fusion noise variance estimators and other parameters estimators are presented by using correlation method and the Gevers-Wouters algorithm with a dead *** have strong ***,a self-tuning weighted measurement fusion Kalman filter based on a self-tuning Riccati equation is *** the the dynamic error system analysis（DESA）method,it is rigorously proved that the self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter in a realization,so that it has asymptotic global *** self-tuning filter can be applied to the signal processing to obtain the self-tuning measurement fusion signal filters.A simulation example of a self-tuning fused filter for ARMA signal with 3-sensor signal shows its effectiveness.

关键词： Multisensor Information Fusion Filter Measurement Fusion Common Noises Self-tuning Fusion Filter Parameter Identification

Descriptor Wiener state fuser

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Descriptor Wiener state fuser

2009中国控制与决策会议

作者： chenjian ran~1,zili deng~2 1.Department of Automation,Heilongjiang University,Harbin 150080 2.Department of Automation,Heilongjiang University,Harbin 150080

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

关键词： Multisensor information fusion Weighted fusion Decoupled fusion Descriptor system Wiener state fuser White noise estimator Modern time series analysis method and ARMA innovation model

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2009中国控制与决策会议

作者： chenjian ran~1,zili deng~2 1.Department of Automation,Heilongjiang University,Harbin 150080 2.Department of Automation,Heilongjiang University,Harbin 150080

＜正＞For the multisensor linear discrete time-invariant systems with correlated measurement noises and with different measurement matrices,based on the weighted least squares（WLS） method,applying the orthogonal transformation, two weighted measurement fusion Kalman filters are *** the information filter,it is proved that they are functionally equivalent to the centralized fusion Kalman filter,*** corresponding two weighted measurement fusion Kalman filters are numerically identical to the centralized fusion Kalman filter,so that they have the global optimality. Compared with the centralized fusion Kalman filter,they can obviously reduce the computational *** numerical simulation examples in the tracking systems verify their functional equivalence and compare their computational load.

关键词： Multisensor information fusion Kalman filter Measurement fusion Correlated measurement noises Global optimality and Orthogonal transformation

Reduced dimension weighted measurement fusion Kalman filtering algorithm

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Reduced dimension weighted measurement fusion Kalman filteri...

2009中国控制与决策会议

作者： chenjian ran ~1,zili deng~2 1.Department of Automation,Heilongjiang University,Harbin 150080 2.Department of Automation,Heilongjiang University,Harbin 150080

＜正＞For the multisensor linear discrete time-invariant systems with correlated measurement noises and with dif ferent measurement matrices,based on the linear unbiased minimum variance criterion,a weighted measurement fusion Kalman filtering algorithm is *** is identical to that obtained by the Weighted Least Squares（WLS） method, and is numerically identical to the centralized fusion Kalman filtering algorithm,so that it has the global *** optimal weights are given by the Lagrange multiplier method,but its computation burden is *** order to reduce the computational burden,a reduced dimension weighted measurement fusion Kalman filtering algorithm is derived,which avoids the Lagrange multiplier method,and can significantly reduced the computational *** comparison of computational count between two algorithms is given.A simulation example shows effectiveness and correctness of the proposed algorithm.

关键词： Weighted measurement fusion Linear unbiased minimum variance(LUMV) criterion Lagrange multiplier method and Reduced dimension algorithm

Convergence analysis of self-tuning Riccati equation for systems with correlation noises

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Frontiers of Electrical and Electronic Engineering in China 2009年第4期4卷 409-416页

作者： chenjian ran Guili TAO Jinfang LIU zili deng Department of Automation Heilongjiang UniversityHarbin 150080China Department of Computer Heilongjiang Institute of Science and TechnologyHarbin 150080China Department of Computer and Information Engineering Harbin Deqiang Business CollegeHarbin 150080China

For linear discrete time-invariant stochastic system with correlated noises,and with unknown state transition matrix and unknown noise statistics,substituting the online consistent estimators of the state transition matrix and noise statistics into steady-state optimal Riccati equation,a new self-tuning Riccati equation is presented.A dynamic variance error system analysis(DVESA)method is presented,which transforms the convergence problem of self-tuning Riccati equation into the stability problem of a time-varying Lyapunov *** decision criterions of the stability for the Lyapunov equation are *** the DVESA method and Kalman filtering stability theory,it proves that with probability 1,the solution of self-tuning Riccati equation converges to the solution of the steady-state optimal Riccati equation or time-varying optimal Riccati *** proposed method can be applied to design a new selftuning information fusion Kalman filter and will provide the theoretical basis for solving the convergence problem of self-tuning filters.A numerical simulation example shows the effectiveness of the proposed method.

关键词： Kalman filter Riccati equation Lyapunov equation self-tuning filter convergence stability dynamic variance error system analysis(DVESA)method

基于网络药理学和分子对接探讨西黄丸治疗宫颈癌的作用机制

维普期刊数据库评论

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维普期刊数据库

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现代药物与临床 2024年第2期39卷 325-334页

作者：邓鸿飞杨自力罗毅南京中医药大学附属中西医结合医院江苏南京210000

目的利用网络药理学和分子对接的方法研究西黄丸治疗宫颈癌的分子机制。方法采用计算机检索TCMSP、BATMAN-TCM数据库,并筛选西黄丸的有效活性成分及作用靶点。检索OMIM、GeneCards数据库,获得宫颈癌的疾病靶点,合并去重后,与西黄丸获得... 详细信息

目的利用网络药理学和分子对接的方法研究西黄丸治疗宫颈癌的分子机制。方法采用计算机检索TCMSP、BATMAN-TCM数据库,并筛选西黄丸的有效活性成分及作用靶点。检索OMIM、GeneCards数据库,获得宫颈癌的疾病靶点,合并去重后,与西黄丸获得的有效成分靶点取交集。运用String数据库构建蛋白质相互作用(PPI)网络图。利用Cytoscape3.9.1软件进行可视化,并采用DAVID数据库对交集基因进行(GO)功能富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。最后运用AutoDock Vina、Pymol软件进行分子对接验证。结果筛选得到西黄丸70个主要活性成分,包括表雄酮、槲皮素、雄甾酮等。与宫颈癌相关的靶点有116个,核心靶点有肿瘤蛋白P53(TP53)、表皮生长因子受体(EGFR)、蛋白激酶B1(Akt1)、白细胞介素(IL)-6、原癌基因(MYC)、转录因子AP-1(JUN)、半胱氨酸蛋白酶3(CASP3)、IL-1B、肿瘤坏死因子(TNF)、雌激素受体1(ESR1)、B淋巴细胞2(Bcl-2)、低氧诱导因子-1A(HIF-1A)、基质金属蛋白酶9(MMP9)、V-Rel网状内皮增生病毒癌基因同源物A(RELA)等。GO和KEGG分析结果显示,西黄丸治疗宫颈癌的重要通路有IL-17、TNF、磷脂酰肌醇3-激酶(PI3K)/Akt、p53、HIF-1等信号通路。分子对接结果显示:西黄丸的主要成分与核心靶点有良好的对接效果。结论西黄丸主要通过调节IL-17、TNF、PI3K/Akt、p53,HIF-1等信号通路治疗宫颈癌。

关键词：西黄丸宫颈癌网络药理学分子对接表雄酮槲皮素雄甾酮肿瘤蛋白P53 白细胞介素-17

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

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Journal of Systems Engineering and Electronics 2010年第4期21卷 666-674页

作者： Xiaojun Sun zili deng Department of Automation Heilongjiang University Harbin 150080 R R. China

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation *** it into the steady-state Riccati equation,the self-tuning Riccati equation is *** the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is *** the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

关键词： multisensor information fusion measurement fusion self-tuning fuser white noise deconvolution asymptotic global optimality Kalman filtering convergence.