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kalman filter gain
    A New Algorithm of Steady-state Kalman Filter Gain
    稳态Kalman滤波器增益的一种新算法
    NEW ALGORITHMS OF STEAD STATE KALMAN FILTER GAIN
    稳态Kalman滤波器增益新算法
    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.
    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。
    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.
    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.
 

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