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kalman滤波增益
相关语句
  kalman filter gain
     ESTIMATION OF STEADY-STATE KALMAN FILTER GAIN
     稳态Kalman滤波增益的估计
短句来源
     TWO NEW ALGORITHMS FOR ESTIMATION OF STEADY-STATE KALMAN FILTER GAIN AND THEIR APPLICATIONS
     稳态Kalman滤波增益估计的两种新算法及其应用
短句来源
     This paper presents a new method for estimating the steady-state Kalman filter gain for linear discrete systems.
     本文提出了求线性离散系统稳态Kalman滤波增益的新方法。
短句来源
     From the flight state equation, Kalman filter gain estimation was obtained by using output residuals and Kalman filter gain Markov parameter vectors.
     首先给出了飞行状态方程 ,其次利用输出残差给出了Kalman滤波增益马尔可夫参量估计 ,并且得到了Kalman滤波增益估计 ,解决了自适应跟踪滤波器问题。
短句来源
  “kalman滤波增益”译为未确定词的双语例句
     From the point of view of time series analysis,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering tech-nique.
     本文从时间序列分析观点,基于观测过程的 CARMA 新息模型,提出了稳态 Kalman 滤波增益估计的两种新算法及相应的自校正 Kalman 滤波器,形成一种新的自适应 Kalman 滤波技术.
短句来源
     For the new extended system, the steady state extended Kalman filtering plus matrix and the steady state extended stochastic optimal control plus matrix are obtained by a linear quadratic Gaussian (LQG) method.
     对扩展后的新系统,运用线性二次高斯(LQG)设计方法计算出扩展Kalman滤波增益阵和扩展随机最优控制增益阵.
短句来源
     A four-degree-of-freedom vehicle vibration model is set up based on bond-graph theory and the state equations are deduced. Selecting the minimum sum energy of vertical and pitching vibration and the energy needed to provide control force as the objective function, adopting the Linear Quadratic Gaussian(LQG) control strategy, the optimal control plus and the Kalman filter plus are obtained.
     基于键合图理论建立了四自由度 1 /2车辆主动悬架的振动模型并推导出状态方程 ,以车身垂直振动、纵向角振动及控制总能量最小为目标函数 ,采用随机线性二次型高斯 ( LQG)控制 ,得到了主动悬架的最优控制增益和 Kalman滤波增益 .
短句来源
     Calculating each value of K(t) at each time on-line are avoided by a steady-state Kalman filter and the calculation compared with the iterative method is simplified.
     采用稳态Kalm an滤波器避免了在线计算Kalm an滤波增益在各时刻的函数值,因而减小了采用迭代法计算Kalm an滤波器的计算负担。
短句来源
  相似匹配句对
     ESTIMATION OF STEADY-STATE KALMAN FILTER GAIN
     稳态Kalman滤波增益的估计
短句来源
     A Generalization of Kalman Filtering Theory
     Kalman滤波理论的推广
短句来源
     ADAPTIVE KALMAN FILTER ALGORITHM
     Kalman滤波的自适应算法
短句来源
     TWO NEW ALGORITHMS FOR ESTIMATION OF STEADY-STATE KALMAN FILTER GAIN AND THEIR APPLICATIONS
     稳态Kalman滤波增益估计的两种新算法及其应用
短句来源
     This paper presents a new method for estimating the steady-state Kalman filter gain for linear discrete systems.
     本文提出了求线性离散系统稳态Kalman滤波增益的新方法。
短句来源
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This paper presents a new method for estimating the steady-state Kalman filter gain for linear discrete systems. It consists of three parts; (i) An ARMAX innovation model is derived using Fadeeva's scheme for computing inverse miatrix; (ii) the moving average parameter matrices in ARMAX innovation model are identified by using Gevers and Wouters' algorithm which ensures the invertability of the innovation model; (iii) a new algorithm for estimating the steady -state filter gain is given, which is simpler than...

This paper presents a new method for estimating the steady-state Kalman filter gain for linear discrete systems. It consists of three parts; (i) An ARMAX innovation model is derived using Fadeeva's scheme for computing inverse miatrix; (ii) the moving average parameter matrices in ARMAX innovation model are identified by using Gevers and Wouters' algorithm which ensures the invertability of the innovation model; (iii) a new algorithm for estimating the steady -state filter gain is given, which is simpler than Tajima's.

本文提出了求线性离散系统稳态Kalman滤波增益的新方法。它由三部份组成:(ⅰ)基于矩阵求逆的Fadeeva公式导出了ARMAX新息模型。(ⅱ)用Gevers和Wouters算法辨识ARMAX新息模型中的滑动平均部份的参数阵,可保证新息模型的可逆性。(ⅲ)给出了一种比Tajima算法更简单的估计稳态滤波增益的算法。

From the point of view of time series analysis,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering tech-nique.New algorithms are simpler than that of Mehra and Tajima.As an application example,self-tuning α-β tracking filter with input estimation is given,and simulation results show the effectivenessof the new algorithms.

本文从时间序列分析观点,基于观测过程的 CARMA 新息模型,提出了稳态 Kalman 滤波增益估计的两种新算法及相应的自校正 Kalman 滤波器,形成一种新的自适应 Kalman 滤波技术.新算法比Mehra 和 Tajima 的算法简单.作为应用例子,对于一个简单的跟踪系统,导出了带输入估计的自校正α-β滤波器,仿真结果说明了新算法的有效性.

Application of State Space Model to Interpretation of Both Thin and Bad Water flooded Zone. WLT, 2001, 25(4):291-293 On the basis of state space model theory and with the use of changes of various log data from the same interpreted unit as well as Kalman filtering gain, the relative changes of water flooded situation among effective pay zones are determined in the same interpreted unit, and hense, further qualitatively identifying the degree of water flooded zone. Reservoir parameters are obtained with...

Application of State Space Model to Interpretation of Both Thin and Bad Water flooded Zone. WLT, 2001, 25(4):291-293 On the basis of state space model theory and with the use of changes of various log data from the same interpreted unit as well as Kalman filtering gain, the relative changes of water flooded situation among effective pay zones are determined in the same interpreted unit, and hense, further qualitatively identifying the degree of water flooded zone. Reservoir parameters are obtained with dual formation water resistivity model. Case histories show that the log interpretation precision of water flooded zone is further improved by the given method herein.

根据状态空间模型理论 ,利用在同一解释单元内多种测井资料的变化梯度及 Kalman滤波增益 ,判断同一解释单元内各有效储层间水淹状况相对变化趋势 ,从而实现了储层水淹状况的定性判别 ,再利用双地层水电阻率模型反求储层参数 ,使水淹层测井解释方法获得进一步发展。

 
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