1) dual unscented Kalman filter
双无轨迹卡尔曼滤波器
2) unscented Kalman filter
无轨迹卡尔曼滤波
1.
In view of control difficulty caused by severe nonlinear performance of brushless DC motor(BLDCM),an observer was designed for estimating the rotor position and velocity of BLDCM by using unscented Kalman filter(UKF) algorithm.
针对无刷直流电机(BLDCM)非线性严重而导致控制困难的问题,利用无轨迹卡尔曼滤波(UKF)算法设计了观测器,以估计无刷直流电机的转子位置和角速度。
2.
The psi-angle model of nonlinear inertial navigation system(INS) alignment for large misalignment error was discussed,and the principle of the unscented Kalman filter(UKF) was analyzed.
讨论了大失准角情况下,惯性导航系统(INS)初始对准的非线性误差模型,分析了无轨迹卡尔曼滤波原理,提出将无轨迹卡尔曼滤波(UKF)技术应用于惯性导航系统初始对准ψ角估计中,进行了静基座状态下的初始对准仿真。
3.
For verifying the performances of extended Kalman filter(EKF) and unscented Kalman filter(UKF) in tightly-coupled integrated navigation system,an INS system error model in the earth-centered earth-fixed(ECEF) frame and a GPS pseudo range/range rate model are proposed to form the system equation and the measurement equation respectively,and then EKF and UKF filtering equations are derived.
为了检验扩展卡尔曼滤波(extended Kalman filter,EKF)与无轨迹卡尔曼滤波(unscented Kalmanfilter,UKF)在紧耦合组合导航系统中的性能,给出了地心地球固连(earth-centered earth-fixed,ECEF)坐标系下惯性导航系统(inertial navigation system,INS)误差方程。
3) Rao-Blackwellised unscented Kalman filter
RB无轨迹卡尔曼滤波
4) Dual UKF
双无迹卡尔曼滤波
6) Unscented kalman filter(UKF)
无迹卡尔曼滤波
1.
The unscented Kalman filter(UKF) model for the system is built up,and a numerical simulation is performed with the software Matlab.
设计了一种采用陀螺罗经和多普勒速度仪组合加GPS间歇校正的水下航行器组合导航系统,建立了该组合导航系统的无迹卡尔曼滤波模型,并利用MATLAB软件对其进行了数学仿真验证。
2.
We aim to eliminate these shortcomings as much as possible with a different and we believe better method by using the unscented Kalman filter(UKF) based SLAM(simultaneous localization and mapping) technique.
针对应答器未校准情况下的水下长基线定位问题,提出了基于无迹卡尔曼滤波的同步定位与地图创建方法。
3.
Its core consists of:(1) we design a variable structure sliding-mode speed controller and a variable structure sliding -mode current controller to replace the traditional speed PI controller and two current PI controllers;(2) we design the unscented Kalman filter(UKF) observer to estimate direct-axis current,quadrature-axis current,load torque,rotor position and speed simultaneously.
针对电机控制中采用的PI调节器对电机参数变化及外加干扰时鲁棒差和无位置传感器控制实现困难等问题,在研究常规永磁同步电机矢量控制策略的基础上,将滑模变结构控制(VSSMC)和无迹卡尔曼滤波(UKF)引入该策略中,用VSSMC分别替代策略中速度PI控制器和2个PI电流控制器,同时利用UKF对电机定子直轴电流、交轴电流、负载转矩、转子位置和转速进行实时估计,提出了一种新颖的基于VSSMC和UKF的永磁同步电机无传感器矢量控制方案。
补充资料：卡尔曼滤波器
分子式：
CAS号：
性质：它是随机系统中一种最著名的最优状态估计器。估计的要求是滤去随机分量使状态估计值x与真实状态值x尽量接近。其结构与状态估计器相似，由模型输出估计值y与实测输出相比较所得的误差，通过校正矩阵来对状态估计值x进行在线校正。但因卡尔曼滤波器的目标函数是状态估计值和真实值误差的二次型函数，从而可求得其最优估计的校正矩阵。
CAS号：
性质：它是随机系统中一种最著名的最优状态估计器。估计的要求是滤去随机分量使状态估计值x与真实状态值x尽量接近。其结构与状态估计器相似，由模型输出估计值y与实测输出相比较所得的误差，通过校正矩阵来对状态估计值x进行在线校正。但因卡尔曼滤波器的目标函数是状态估计值和真实值误差的二次型函数，从而可求得其最优估计的校正矩阵。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条