Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the ma...Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system.展开更多
The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the a...The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the aero-engine.Because of the complex environment interference,EGTM always has strong randomness,and the state space based degradation model can identify the noisy observation from the true degradation state,which is more close to the actual situations.Therefore,a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life(RUL).As one of the most effective methods for both linear state estimation and parameter estimation,Kalman filter(KF)is applied.Firstly,with EGTM degradation data,state space model approach is used to set up a state space model for aero-engine.Secondly,RUL of aero-engine is analyzed,and expected RUL and distribution of RUL are determined.Finally,the sate space model and KF algorithm are applied to an example of CFM-56aero-engine.The expected RUL is predicted,and corresponding probability density distribution(PDF)and cumulative distribution function(CDF)are given.The result indicates that the accuracy of RUL prediction reaches 7.76%ahead 580 flight cycles(FC),which is more accurate than linear regression,and therefore shows the validity and rationality of the proposed method.展开更多
This article deals with the problem of maneuvering target tracking which results in a mixed linear/non-linear model estimation problem. For maneuvering tracking system, extended Kalman filter （EKF） or particle filt...This article deals with the problem of maneuvering target tracking which results in a mixed linear/non-linear model estimation problem. For maneuvering tracking system, extended Kalman filter （EKF） or particle filter （PF） is traditionally used to estimate the states. In this article, marginalized particle filter （MPF） is presented for application in a mixed linear/non-linear model estimation problem. MPF is a combination of Kalman filter （KF） and PF. So it holds both advantage of them and can be used for mixed linear/non-linear substructure, where the conditionally linear states are estimated using KF and the nonlinear states are estimated using PF. Simulation results show that MPF guarantees the estimation accuracy and alleviates the potential computational burden problem compared with PF and EKF in maneuvering target tracking application.展开更多
为深入研究我国农村居民的消费行为,揭示我国农村居民消费的行为特征及变化趋势,探讨增加农村居民收入、拓展农村消费的政策作用机制。采用经济计量学的研究方法,分别基于一元线性系统和一元非线性系统,构建我国农村居民消费行为的状态...为深入研究我国农村居民的消费行为,揭示我国农村居民消费的行为特征及变化趋势,探讨增加农村居民收入、拓展农村消费的政策作用机制。采用经济计量学的研究方法,分别基于一元线性系统和一元非线性系统,构建我国农村居民消费行为的状态空间模型(State Space Model),并基于模型对相关时变参数进行卡尔曼滤波(Kalman filter)估计,详细地分析了我国农村居民1978—2010年间平均消费倾向(APC)和边际消费倾向(MPC)的变动趋势及相互关系;同时,对模型作了序列相关性检验、异方差检验和协整检验,以确保模型的合理性和有效性。通过对状态空间模型时变参数的估计,详细分析了我国农村居民消费行为的静态特征和动态特征及其相互关系,较好地解释了外部环境和内在动机对我国农村居民消费行为的影响机制。研究表明:我国农村居民消费行为的状态空间模型是合理的、有效的;农村居民消费行为表现为消费心理预期波动上行,消费倾向偏离趋于缩小,符合我国农村社会经济发展的客观现实。展开更多
基金Supported by the National Natural Science Foundation of China(No.61771006)the Open Foundation of Key Laboratory of Spectral Imaging Technology of the Chinese Academy of Sciences(No.LSIT201711D)+1 种基金the Outstanding Young Cultivation Foundation of Henan University(No.0000A40366)the Excellent Chinese and Foreign Youth Exchange Programme of China Science and Technology Association(2017CASTQNJL046)
文摘Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system.
文摘The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the aero-engine.Because of the complex environment interference,EGTM always has strong randomness,and the state space based degradation model can identify the noisy observation from the true degradation state,which is more close to the actual situations.Therefore,a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life(RUL).As one of the most effective methods for both linear state estimation and parameter estimation,Kalman filter(KF)is applied.Firstly,with EGTM degradation data,state space model approach is used to set up a state space model for aero-engine.Secondly,RUL of aero-engine is analyzed,and expected RUL and distribution of RUL are determined.Finally,the sate space model and KF algorithm are applied to an example of CFM-56aero-engine.The expected RUL is predicted,and corresponding probability density distribution(PDF)and cumulative distribution function(CDF)are given.The result indicates that the accuracy of RUL prediction reaches 7.76%ahead 580 flight cycles(FC),which is more accurate than linear regression,and therefore shows the validity and rationality of the proposed method.
基金supported by the Science Project of Chongqing Educational Committee (KJ080520)the Natural Science Foundation of Chongqing CSTC (CSTC,2008BB2412)
文摘This article deals with the problem of maneuvering target tracking which results in a mixed linear/non-linear model estimation problem. For maneuvering tracking system, extended Kalman filter （EKF） or particle filter （PF） is traditionally used to estimate the states. In this article, marginalized particle filter （MPF） is presented for application in a mixed linear/non-linear model estimation problem. MPF is a combination of Kalman filter （KF） and PF. So it holds both advantage of them and can be used for mixed linear/non-linear substructure, where the conditionally linear states are estimated using KF and the nonlinear states are estimated using PF. Simulation results show that MPF guarantees the estimation accuracy and alleviates the potential computational burden problem compared with PF and EKF in maneuvering target tracking application.
文摘为深入研究我国农村居民的消费行为,揭示我国农村居民消费的行为特征及变化趋势,探讨增加农村居民收入、拓展农村消费的政策作用机制。采用经济计量学的研究方法,分别基于一元线性系统和一元非线性系统,构建我国农村居民消费行为的状态空间模型(State Space Model),并基于模型对相关时变参数进行卡尔曼滤波(Kalman filter)估计,详细地分析了我国农村居民1978—2010年间平均消费倾向(APC)和边际消费倾向(MPC)的变动趋势及相互关系;同时,对模型作了序列相关性检验、异方差检验和协整检验,以确保模型的合理性和有效性。通过对状态空间模型时变参数的估计,详细分析了我国农村居民消费行为的静态特征和动态特征及其相互关系,较好地解释了外部环境和内在动机对我国农村居民消费行为的影响机制。研究表明:我国农村居民消费行为的状态空间模型是合理的、有效的;农村居民消费行为表现为消费心理预期波动上行,消费倾向偏离趋于缩小,符合我国农村社会经济发展的客观现实。
