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
针对当前电力系统中数据采集与监视控制系统(Supervisory Control And Date Acquisition,SCADA)和相量测量单元(Phasor Measurement Units,PMU)并存,以及不同规格的PMU之间不能实现完全同步的现状,引入了一种基于不完全同步PMU的电力系...针对当前电力系统中数据采集与监视控制系统(Supervisory Control And Date Acquisition,SCADA)和相量测量单元(Phasor Measurement Units,PMU)并存,以及不同规格的PMU之间不能实现完全同步的现状,引入了一种基于不完全同步PMU的电力系统动态状态估计模型,与SCADA组成混合量测系统,在此基础上提出了一种考虑相位失配量的线性动态状态估计算法.结合线性定常系统卡尔曼滤波原理,分别对相位失配量和系统状态进行滤波处理,实现对系统状态的估计.相比于传统方法,该算法的雅可比矩阵为常数阵,缩短了状态估计时间,减少了不必要的迭代.采用Matlab平台在IEEE 30节点系统上进行仿真实验,结果表明,在PMU不完全同步的情况下,提出的方法较传统的线性算法具有更好的性能,在很大程度上减小了状态估计产生的误差.展开更多
在对脑电信号进行采集与处理的过程中,采集到的脑电信号在经过硬件处理之后仍然包含有大量的噪声,必须进行滤波处理.本文介绍了卡尔曼滤波的基本原理,阐述了Matlab的优点与缺点,介绍了目前流行的Visual studio 2010工具平台的新特性,提...在对脑电信号进行采集与处理的过程中,采集到的脑电信号在经过硬件处理之后仍然包含有大量的噪声,必须进行滤波处理.本文介绍了卡尔曼滤波的基本原理,阐述了Matlab的优点与缺点,介绍了目前流行的Visual studio 2010工具平台的新特性,提出了采用Visual studio 2010与Matlab R2012b混合编程实现卡尔曼滤波的方法.通过实例,利用Visual studio 2010与Matlab R2012b混合编程实现了对白鼠脑电信号的卡尔曼滤波,取得了良好的效果.该方法将两者的优缺点进行了互补,实现了Matlab强大的数值运算能力的跨平台应用,使所得程序在处理复杂运算时的运算速度比单独使用C、C++等语言进行处理要快,同时又有用户界面良好的优点,在科研工作和工程开发中的应用前景非常广阔.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.
文摘针对当前电力系统中数据采集与监视控制系统(Supervisory Control And Date Acquisition,SCADA)和相量测量单元(Phasor Measurement Units,PMU)并存,以及不同规格的PMU之间不能实现完全同步的现状,引入了一种基于不完全同步PMU的电力系统动态状态估计模型,与SCADA组成混合量测系统,在此基础上提出了一种考虑相位失配量的线性动态状态估计算法.结合线性定常系统卡尔曼滤波原理,分别对相位失配量和系统状态进行滤波处理,实现对系统状态的估计.相比于传统方法,该算法的雅可比矩阵为常数阵,缩短了状态估计时间,减少了不必要的迭代.采用Matlab平台在IEEE 30节点系统上进行仿真实验,结果表明,在PMU不完全同步的情况下,提出的方法较传统的线性算法具有更好的性能,在很大程度上减小了状态估计产生的误差.
文摘在对脑电信号进行采集与处理的过程中,采集到的脑电信号在经过硬件处理之后仍然包含有大量的噪声,必须进行滤波处理.本文介绍了卡尔曼滤波的基本原理,阐述了Matlab的优点与缺点,介绍了目前流行的Visual studio 2010工具平台的新特性,提出了采用Visual studio 2010与Matlab R2012b混合编程实现卡尔曼滤波的方法.通过实例,利用Visual studio 2010与Matlab R2012b混合编程实现了对白鼠脑电信号的卡尔曼滤波,取得了良好的效果.该方法将两者的优缺点进行了互补,实现了Matlab强大的数值运算能力的跨平台应用,使所得程序在处理复杂运算时的运算速度比单独使用C、C++等语言进行处理要快,同时又有用户界面良好的优点,在科研工作和工程开发中的应用前景非常广阔.
