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稳态kalman滤波器     
相关语句
  steady-state kalman filter
     Multisensor Optimal Information Fusion Steady-state Kalman Filter
     多传感器最优信息融合稳态Kalman滤波器
短句来源
     Two-sensor Information Fusion Steady-state Kalman Filter and Smoother
     两传感器信息融合稳态Kalman滤波器和平滑器
短句来源
     Multi-sensor optimal information fusion steady-state Kalman filter weighted by scalars
     多传感器标量加权最优信息融合稳态Kalman滤波器
短句来源
     Using the modern time series analysis method, based on Ihe antoregressive moving average (ARMA) innovationmodel, two-sensor optimal information fusion steady-state Kalman filter is presented.
     应用现代时间序列分析方法,基于ARMA新息模型提出了两传感器最优信息融合稳态Kalman滤波器
短句来源
     Using steady-state Kalman filtering theory, a multi-sensor optimal information fusion steady-state Kalman filter is given based on this fusion criterion.
     运用稳态Kalman滤波理论,基于该融合准则,给出了多传感器最优信息融合稳态Kalman滤波器.
短句来源
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  steady kalman filter
     A New Steady Kalman Filter in General System
     广义系统新的稳态Kalman滤波器
短句来源
  steady state kalman filter
     NEW ALGORITHMS OF STEADY STATE KALMAN FILTER FOR STOCHASTIC CONTROL SYSTEMS
     随机控制系统稳态Kalman滤波器新算法
短句来源
     Using the modern time series analysis method, based on the controlled autoregressive moving average(CARMA) innovation model, two new algorithms of steady state Kalman filter gain for stochastic control systems are presented, where the solution of the Riccati equation is avoided.
     应用现代时间序列分析方法,基于受控的自回归滑动平均(CARMA)新息模型,提出了随机控制系统稳态Kalman滤波器增益的两种新算法,避免了求解Riccati方程.
短句来源
     By using the modern time series analysis method and based on the ARMA innovation model,two new algorithms of steady state Kalman filter gain are presented,and their equivalence is proved. The self tuning Kalman filters can be implemented by using a recursive identifier of parameters for the ARMA innovation model,in conjunction with the new algorithms. A simulation example shows usefulness of the proposed algorithms.
     应用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增益的两种简单的新算法,并证明了它们的等价性.应用ARMA新息模型参数的递推辨识器伴随新算法,可实现自校正Kalman滤波器.仿真例子说明了其有效性.
短句来源
  “稳态kalman滤波器”译为未确定词的双语例句
     NEW ALGORITHMS OF STEAD STATE KALMAN FILTER GAIN
     稳态Kalman滤波器增益新算法
短句来源
     Multi-sensor optimal information fusion steady-state Kalman filterweighted by scalars for systems with colored measurement noises
     带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器
短句来源
     For the systems with multisensor, applying the information filtering principle, based on the modified Riccati equation , inverse prediction error variance matrix equation, and inverse filtering error variance matrix equation, the corresponding three fast algorithms for time-varying and steady state Kalman filters based on the centralized measurement fusion are presented , which avoid the high dimensional inverse matrix,and obviously reduce the computational burden.
     对于多传感器系统,应用信息滤波原理,基于改进的Riccati方程、逆预报误差方差阵方程和逆滤波误差方差阵方程,提出了相应的集中式观测融合时变和稳态Kalman滤波器的三种快速算法,可避免高维矩阵求逆,明显减小计算负担。
短句来源
     Based on the multi_sensor optimal information fusion criterion weighted by scalars in the linear minimum variance,a scalar weighting information fusion steady_state Kalman filter with a two_layer fusion structure is given for discrete linear stochastic control systems measured by multiple sensors with colored measurement noises,which is equivalent to an optimal information fusion steady_state Kalman predictor for the corresponding systems with correlated noises.
     基于标量加权多传感器线性最小方差最优信息融合准则,对被多传感器观测的带有色观测噪声的离散线性随机控制系统,提出了一种具有两层融合结构的标量加权信息融合稳态Kalman滤波器,它等价于相应的带相关噪声系统的最优信息融合稳态Kalman预报器.
短句来源
     And Lyapunov equations are presented for computing the filtering error variance and covariance matrices among sensors, which can be solved by iteration. The exponential convergence of the iterative solution is proved.
     基于ARMA新息模型计算稳态Kalman滤波器增益,提出了计算传感器之间的滤波误差方差阵和协方差阵的Lyapunov方程,它可用迭代法求解,并证明了迭代解的指数收敛性.
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  steady-state kalman filter
An ensemble Kalman filter-based steady-state Kalman filter is developed for assimilation of salinity and horizontal currents into an existing three-dimensional flow model for the highly non-linear stratified shallow bay.
      
It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter.
      
  其他


This paper presents two modified stationary Kalman filters for discrete -time systems with improved transient performance.A linear, time-in variant, discrete-time system, whose state is to be estimated, is characterized by equations (1) and (2). The linear, time -invariant state estimator is characterized by equations (8) and (9). The filter should be so designed as to take into account different operating conditions. There are three cases to be considered,1.When the transient performance is principally emphasized,...

This paper presents two modified stationary Kalman filters for discrete -time systems with improved transient performance.A linear, time-in variant, discrete-time system, whose state is to be estimated, is characterized by equations (1) and (2). The linear, time -invariant state estimator is characterized by equations (8) and (9). The filter should be so designed as to take into account different operating conditions. There are three cases to be considered,1.When the transient performance is principally emphasized, the problem is that of designing a state observer.2.When the steady-state accuracy is principally emphasized, the problem is that of designing a classical Kalman filter.3.When a compromise between the steady-state and the transient performances is sought, two new performance measures (28) and (34) are defined. And thus two modified Kalman filters are obtained as shown in Theorems 2-5.

本文研究线性定常离散时间系统当系统噪声和测量噪声为平稳白噪声序列时,为改进稳态Kalman滤波器瞬态性能的定常状态估计器的设计方法。文中提出了滤波器瞬态性能的度量方法;提出了两种新的性能函数的定义,从而给出两种瞬态性能得到改善的、适用于短时间运行的定常状态估计器。

Using the modern time series analysis, based on the ARMA innovation model. this paper presents a new algorithm of steady-state Kalman filter gain. which can handle systems with the correlated model and observation noises, and also can handle systems with singular and/or unstable state transition matrix. A simulation example shows its usefulness.

用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增益阵的一种新算法。可处理模型噪声与观测噪声相关的系统,也可处理带奇异的和/或不稳定状态转移阵的系统。仿真例子说明了其有效性。

By using the modern time series analysis method and based on the ARMA innovation model,two new algorithms of steady state Kalman filter gain are presented,and their equivalence is proved.The self tuning Kalman filters can be implemented by using a recursive identifier of parameters for the ARMA innovation model,in conjunction with the new algorithms.A simulation example shows usefulness of the proposed algorithms.

应用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增益的两种简单的新算法,并证明了它们的等价性.应用ARMA新息模型参数的递推辨识器伴随新算法,可实现自校正Kalman滤波器.仿真例子说明了其有效性.

 
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