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kalman增益
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  kalman gain
    The purpose of this paper is 1) to propose a Kalman-filtering-formula based ARMA spectral estimator capable of estimating spectra on line, 2)to consider a fast algorithm for recursively calculating the Kalman gain whose computational requirement is proportional to the order of the filter, and 3) to give a consistency theorem for this recursive estimator.
    本文从Kalman滤波的角度提出了一种ARMA谱的自适应估计法,导出了适用于ARMA模型的时间递归Kalman增益快速计算法,其计算量与滤波器的阶数成正比,并给出了些ARMA谱估计法的一致收敛性定理。
短句来源
  kalman gain
    The purpose of this paper is 1) to propose a Kalman-filtering-formula based ARMA spectral estimator capable of estimating spectra on line, 2)to consider a fast algorithm for recursively calculating the Kalman gain whose computational requirement is proportional to the order of the filter, and 3) to give a consistency theorem for this recursive estimator.
    本文从Kalman滤波的角度提出了一种ARMA谱的自适应估计法,导出了适用于ARMA模型的时间递归Kalman增益快速计算法,其计算量与滤波器的阶数成正比,并给出了些ARMA谱估计法的一致收敛性定理。
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  kalman gain
Stable covariance propagation relationships are presented and the transformed Kalman gain is also furnished and its use in the determination of the externally updated error states is discussed.
      
Moreover, it enables a better adaptation of the Kalman gain to the actual estimation error.
      
The MEKF does not encounter singularities when computing the Kalman gain and it can estimate simultaneously the translation and rotation transformations.
      
A Kalman gain is used to determine if more weight is put on the measured contour versus the model prediction.
      
As a consequence also the Kalman gain was updated at each time step.
      
更多          
  kalman gain
Stable covariance propagation relationships are presented and the transformed Kalman gain is also furnished and its use in the determination of the externally updated error states is discussed.
      
Moreover, it enables a better adaptation of the Kalman gain to the actual estimation error.
      
The MEKF does not encounter singularities when computing the Kalman gain and it can estimate simultaneously the translation and rotation transformations.
      
A Kalman gain is used to determine if more weight is put on the measured contour versus the model prediction.
      
As a consequence also the Kalman gain was updated at each time step.
      
更多          


The purpose of this paper is 1) to propose a Kalman-filtering-formula based ARMA spectral estimator capable of estimating spectra on line, 2)to consider a fast algorithm for recursively calculating the Kalman gain whose computational requirement is proportional to the order of the filter, and 3) to give a consistency theorem for this recursive estimator.Compared with recently developed ARMA spectral estimation approaches--the overdeterminedequation method and the instrumental variable method, this estimator...

The purpose of this paper is 1) to propose a Kalman-filtering-formula based ARMA spectral estimator capable of estimating spectra on line, 2)to consider a fast algorithm for recursively calculating the Kalman gain whose computational requirement is proportional to the order of the filter, and 3) to give a consistency theorem for this recursive estimator.Compared with recently developed ARMA spectral estimation approaches--the overdeterminedequation method and the instrumental variable method, this estimator has advantages that the positivity of estimated spectra can be warranteed and parameters of ARMA processes can be identified in real time. Numerical examples show that this estimator provides higher resolution than AR spectral estimation.

本文从Kalman滤波的角度提出了一种ARMA谱的自适应估计法,导出了适用于ARMA模型的时间递归Kalman增益快速计算法,其计算量与滤波器的阶数成正比,并给出了些ARMA谱估计法的一致收敛性定理。较诸常用的超定方程法和辅助变量法,本方法具有能保证估计谱的正性、且能实时地识别出ARMA过程参量的优点。数值计算表明,所述方法有着良好的分辨率。

A fast recursive total least squares algorithm is developed to allow recursive computation of total least squares solution for adaptive finite impulse response (FIR) filters. The augmented data vector is used as search vector for the iterative algorithm. Using the shift structure of the augmented data vector, the fast algorithm for the gain vector is given. Unlike fast algorithm of Kalman gain vector, less operations per time step is necessary, while being stable numerically. The operations for extracting the...

A fast recursive total least squares algorithm is developed to allow recursive computation of total least squares solution for adaptive finite impulse response (FIR) filters. The augmented data vector is used as search vector for the iterative algorithm. Using the shift structure of the augmented data vector, the fast algorithm for the gain vector is given. Unlike fast algorithm of Kalman gain vector, less operations per time step is necessary, while being stable numerically. The operations for extracting the smallest eigenvalue and the corresponding eigenvectors of the augmented data correlation matrix is O(N) and less than the ones of Davila′s RTLS algorithm per time step. The performance of the algorithms is evaluated via simulations.

提出了一种新的递归全局最小二乘快速算法,其可用于递归计算自适应(FIR)滤波问题的全局最小二乘(TLS)解.在这个算法中,以增广数据矢量为优化搜索方向,其快速计算归结为新定义的增益矢量的快速计算.利用数据矢量的位移结构找到了计算增益矢量的快速算法,它的运算量比Kalman增益矢量的快速算法的运算量少,且不存在数值计算不稳定性问题.在追踪与增广协方差矩阵的最小特征值相关联的特征矢量过程中,这个算法每步须O(N)乘法运算,比Davila的RTLS算法的运算量少.由计算机仿真比较了有关算法的性能

 
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