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稳态kalman滤波器
相关语句
  steady-state kalman filter
    A New Algorithm of Steady-state Kalman Filter Gain
    稳态Kalman滤波器增益的一种新算法
短句来源
    A STEADY-STATE KALMAN FILTER AND ITS ASYMPTOTIC STABILITY
    一类稳态Kalman滤波器及其渐近稳定性
短句来源
    Using the modern time series analysis, based on the ARMA innovation model. this paper presents a new algorithm of steady-state Kalman filter gain.
    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。
短句来源
    Using the modern time series analysis method,based on the ARMA innovation model and white noise estimators, a steady-state Kalman filter is presented for completely observable discrete linear stochastic systems,where two new algorithms of steady-state Kalman filter gain are given, and a formula of setting the initial value of filter is given to ensure asymptotic stability of filter. A simulation example shows its usefulness. 
    应用现代时间序列分析方法,基于ARMA新息模型和白噪声估值器,对完全可观的离散线性随机系统,提出了一类稳态Kalman滤波器,其中给出了稳态Kalman滤波器增益的两种新算法,并给出了保证滤波器渐近稳定性的初值选择公式.仿真例子说明了其有效性.
短句来源
    The autoregressive moving average( ARMA) innovation model is yielded by steady-state Kalman filter, and the recursive versions of non-recursive steady-state optimal Kalman state estimators yield the Wiener state estimators, which can solve the filtering, prediction and smoothing problems in a unified framework. They have the state-decoupling ARMA recursive forms, and have asymptotic stability and optimality. Simulation results show their effectiveness.
    通过稳态Kalman滤波器建立ARMA新息模型,由稳态最优非递推Kalman状态估值器的递推变形引出Wiener状态估值器,可统一处理滤波、预报和平滑问题,它们具有状态解耦的ARMA递推形式,且具有渐近稳定性和最优性,仿真结果表明了算法的有效性。
短句来源
更多       
  steady-state kalman filter
    A New Algorithm of Steady-state Kalman Filter Gain
    稳态Kalman滤波器增益的一种新算法
短句来源
    A STEADY-STATE KALMAN FILTER AND ITS ASYMPTOTIC STABILITY
    一类稳态Kalman滤波器及其渐近稳定性
短句来源
    Using the modern time series analysis, based on the ARMA innovation model. this paper presents a new algorithm of steady-state Kalman filter gain.
    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。
短句来源
    Using the modern time series analysis method,based on the ARMA innovation model and white noise estimators, a steady-state Kalman filter is presented for completely observable discrete linear stochastic systems,where two new algorithms of steady-state Kalman filter gain are given, and a formula of setting the initial value of filter is given to ensure asymptotic stability of filter. A simulation example shows its usefulness. 
    应用现代时间序列分析方法,基于ARMA新息模型和白噪声估值器,对完全可观的离散线性随机系统,提出了一类稳态Kalman滤波器,其中给出了稳态Kalman滤波器增益的两种新算法,并给出了保证滤波器渐近稳定性的初值选择公式.仿真例子说明了其有效性.
短句来源
    The autoregressive moving average( ARMA) innovation model is yielded by steady-state Kalman filter, and the recursive versions of non-recursive steady-state optimal Kalman state estimators yield the Wiener state estimators, which can solve the filtering, prediction and smoothing problems in a unified framework. They have the state-decoupling ARMA recursive forms, and have asymptotic stability and optimality. Simulation results show their effectiveness.
    通过稳态Kalman滤波器建立ARMA新息模型,由稳态最优非递推Kalman状态估值器的递推变形引出Wiener状态估值器,可统一处理滤波、预报和平滑问题,它们具有状态解耦的ARMA递推形式,且具有渐近稳定性和最优性,仿真结果表明了算法的有效性。
短句来源
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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.
      
  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.
      


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。

Using the modern time series analysis method,based on the ARMA innovation model and white noise estimators, a steady-state Kalman filter is presented for completely observable discrete linear stochastic systems,where two new algorithms of steady-state Kalman filter gain are given, and a formula of setting the initial value of filter is given to ensure asymptotic stability of filter. A simulation example shows its usefulness.

应用现代时间序列分析方法,基于ARMA新息模型和白噪声估值器,对完全可观的离散线性随机系统,提出了一类稳态Kalman滤波器,其中给出了稳态Kalman滤波器增益的两种新算法,并给出了保证滤波器渐近稳定性的初值选择公式.仿真例子说明了其有效性.

Based on classical steady-state Kalman filtering theory, a new approach of designing optimal Wiener state estimators is presented for the system with white and colored observation noises. The autoregressive moving average( ARMA) innovation model is yielded by steady-state Kalman filter, and the recursive versions of non-recursive steady-state optimal Kalman state estimators yield the Wiener state estimators, which can solve the filtering, prediction and smoothing problems in a unified framework. They have the...

Based on classical steady-state Kalman filtering theory, a new approach of designing optimal Wiener state estimators is presented for the system with white and colored observation noises. The autoregressive moving average( ARMA) innovation model is yielded by steady-state Kalman filter, and the recursive versions of non-recursive steady-state optimal Kalman state estimators yield the Wiener state estimators, which can solve the filtering, prediction and smoothing problems in a unified framework. They have the state-decoupling ARMA recursive forms, and have asymptotic stability and optimality. Simulation results show their effectiveness.

基于经典稳态Kalman滤波理论,对带白色和有色观测噪声系统提出了设计最优Wiener状态估值器的新方法。通过稳态Kalman滤波器建立ARMA新息模型,由稳态最优非递推Kalman状态估值器的递推变形引出Wiener状态估值器,可统一处理滤波、预报和平滑问题,它们具有状态解耦的ARMA递推形式,且具有渐近稳定性和最优性,仿真结果表明了算法的有效性。

 
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