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1)  AEKF
加性扩展Kalman滤波器
1.
The additive extended Kalman filter(AEKF) is designed to estimate the satellite attitude accurately.
设计了加性扩展Kalman滤波器(AEKF),对卫星姿态进行了准确估计。
2)  Extended Kalman Filter
扩展Kalman滤波器
1.
Extended Kalman filter (EKF) is a nonlinear estimation method based on optimal control theory.
扩展Kalman滤波器考虑了系统噪声和测量噪声的影响,是一种基于最优控制理论的非线性估计方法。
2.
A speed sensorless vector control method of induction motor using extended Kalman filter (EKF) was proposed to overcome the shortcomings of the classical EKF, such as the high dimensionality of the iterative matrixes and the large amount of calculation.
为了解决传统扩展Kalman滤波器(EKF)迭代矩阵维数高、计算量大的缺点,提出了一种新的基于扩展Kalman滤波器的异步电机无速度传感器矢量控制方法。
3.
The rotor position and speed of a permanent magnet synchronous motor(PMSM) are estimated for mechanical sensor-less vector control by an extended Kalman filter(EKF) algorithm suitable for solving the nonlinear PMSM equations.
为了准确估计永磁同步电机(PMSM)的转子位置和转速,并实现无机械传感器矢量控制,提出了一种基于扩展Kalman滤波器(EKF)的转子位置和转速观测器,适合于求解永磁同步电机的非线性方程。
3)  Extended Kalman filter
扩展Kalman滤波
1.
The minimum meansquare values currently used in fault diagnosis can only get the static estimate values,and although the extended Kalman filter(EKF) can realize dynamic parameter estimation,yet for a nonlinear system,a single EKF does not exhibit good ability for either the normal process or the fault process.
如果采用最小二乘算法,参数估计是静态的,故障诊断延迟一般较大;采用单模型扩展Kalman滤波算法,虽然能够实现动态估计,但不能同时兼顾稳态过程和过渡过程(突发故障)的参数估计,导致误差较大。
2.
A novel learning algorithm for wavelet neural network based on Extended Kalman Filter is proposed to predict the deformation of structure.
提出一种新颖的用于变形预测的基于扩展Kalman滤波的小波神经网络学习算法,与BP算法相比,该方法具有更好的收敛率和学习能力,并通过实例计算证明了该方法具有较高的精度和较快的计算速度。
3.
A new learning algorithm for a multilayered neural network based on extended Kalman filter is proposed to predict the deformation of structure.
给出了一种用于变形预测的基于扩展Kalman滤波的神经网络学习算法,与BP算法相比,该方法具有更好的收敛率和学习能力。
4)  extended Kalman filter(EKF)
扩展Kalman滤波
1.
It is shown,by comparison and analysis that the UKF algorithm is better than extended Kalman filter(EKF) and the adaptive EKF is superior to UKF,and the adaptive UKF is superior to all algorit.
计算结果表明,在GPS/INS松组合导航系统数据处理时,UKF算法略优于扩展Kalman滤波(EKF),自适应UKF算法优于自适应EKF算法,自适应UKF算法能够很好地抑制动力学模型误差对导航解的影响,进一步提高导航解的精度和可靠性。
2.
At present the relatively widely used terrain aided navigation(TAN) method is based on extended Kalman filter(EKF).
传统地形辅助导航中应用较为广泛的是扩展Kalman滤波方法,但因为地形高度测量的非线性,扩展Kalman滤波不一定能够得到系统最优估计。
3.
Secondly,a spherical-coordinate based extended Kalman filter(EKF)is adopted to estimate the delay range and the smaller ones can be viewed as being from physical targets while the larger ones can be viewed as active decoys.
首先,将假目标的延迟距离增广到球坐标系的状态矢量中,从理论上导出了弹道目标更一般意义下的运动方程;其次,利用球坐标下的扩展Kalman滤波(EKF)对延迟距离进行实时估计,延迟距离较小的判定为实体目标,延迟距离较大的判定为有源假目标;最后,分析了相关因素对跟踪和鉴别性能的影响。
5)  extend Kalman filter
扩展Kalman滤波
1.
In order to choose reasonable strategies to deal with nonlinear models in filter, the extend Kalman filter, Bancroft algorithm and refined measure equationsby using surveying information are analyzed and com-.
为此,分别讨论了扩展Kalman滤波和Bancroft算法以及利用观测信息迭代精化观测方程三种算法,并结合算例进行了比较与分析。
2.
The autonomous navigation is implemented by Extend Kalman Filter (EKF) using the selenocentric direction and the selenocentric distance, which are measured by the ultraviolet moon sensor and the altimeter respectively.
本文以f-g 级数形式推导出了小时问间隔情况下的月球探测器状态传播方程,研究了以紫外月球敏感器测量的月心方向矢量和测高仪测得的月心距作为观测量,采用扩展Kalman滤波估计月球探测器位置和速度的自主导航技术,并给出了数字仿真分析。
6)  EKF
扩展Kalman滤波
补充资料:波数字滤波器
      由两端终接电阻负载的无源LC梯形滤波器导出的一种数字滤波器结构。应用双线性变换S=k(1-Z-1)/(1+Z-1)可将用波参数描述的无源元件,实现为数字元件。表1列出了无源元件R、C、L及其对应的由延时单元、乘法器和加法器构成的数字元件。
  
  
  通常高阶递归型数字滤波器的级联型或并联型有产生极限环振荡的危险。此外,在输入信号非常小时,由于舍入误差的高度相关,也可能导致数字滤波器的不稳定而产生固定振荡。1971年,提出波数字滤波器的伪无源性概念,并论证了这种数字滤波器的低灵敏度特性。波数字滤波器具有良好的稳定性,可以用较短的系数字长实现,有良好的动态范围,不会出现极限环振荡(即在环路情况下应用也不会出现任何杂散振荡)。这种数字滤波器特别适用于通信系统。
  
  在波数字滤波器结构中,用波参数描述的元件,在联接时必须遵从端口间阻抗匹配的原则。相应地,数字元件之间的联接按其为串联或并联分别采用由乘法器和加法器构成的串联适配器或并联适配器匹配联接。适配器符号及其对应的联接关系见表2。图为三阶椭圆型低通LC 梯形滤波器及其等效的波数字滤波器。  波数字滤波器的另一种结构称为波格型数字滤波器,是由对称型或格型无源LC 滤波器导出的。
  
  

参考书目
   A. Antonious, Digital Filter : Analysis and Design,McGraw-Hill Co.,New York,1979.
  

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