1) sequential extended Kalman filter
序贯扩展卡尔曼滤波
2) sequential Kalman filter
序贯卡尔曼滤波
3) EKF
扩展卡尔曼滤波
1.
Estimation of Rotor Position and Velocity of Brushless DC Motor with an EKF Method;
用扩展卡尔曼滤波器估计无刷直流电机转子位置和转速
2.
Cooperative Multi-robot Localization Based on EKF;
基于扩展卡尔曼滤波的多机器人协作定位
3.
Integrated Navigation Algorithm Based on IMM-EKF
基于多模型扩展卡尔曼滤波的组合导航算法
4) extended kalman filter
扩展卡尔曼滤波
1.
Pipeline fluid monitoring and leak location based on hydraulic transient and extended kalman filter;
基于水力瞬变与扩展卡尔曼滤波的管道流体监测与泄漏定位
2.
Attitude estimation based on extended Kalman filter for a two-wheeled robot;
基于扩展卡尔曼滤波的两轮机器人姿态估计
3.
Application of extended Kalman filter in parameter identification of dynamic load model;
扩展卡尔曼滤波在动态负荷参数辨识中应用
5) extended kalman filtering
扩展卡尔曼滤波
1.
Relative attitude determination based on extended Kalman filtering(EKF) is presented.
针对编队飞行中从飞行器与主飞行器的相对姿态确定问题,提出了基于扩展卡尔曼滤波(EKF)的相对姿态确定方法。
2.
It extracts and tracks feature point sets in the environment with single camera,and then calculates position and pose of the robot with measurement model and extended Kalman filtering.
利用单目摄像头提取和跟踪环境特征点集,进而根据观测模型利用扩展卡尔曼滤波算法估算出机器人的位姿。
3.
The effect of colored Kalman filtering is compared with extended Kalman filtering (EKF).
基于递推最小二乘法设计并实现海浪干扰力和干扰力矩的成型波滤器 ,对有色的次优卡尔曼滤波和扩展卡尔曼滤波 ( EKF)的效果进行对比。
6) Extended kalman filter(EKF)
扩展卡尔曼滤波
1.
In view of the problem that the linearization process of extended Kalman filter(EKF) can introduce some errors into the system,the unscented Kalman filter(UKF) was utilized to design the system.
针对扩展卡尔曼滤波(EKF)在线性化过程中会引入误差的问题,采用平淡卡尔曼滤波器(UKF)进行了系统滤波器设计;提出一种构建虚拟观测量的方法,并分析了其噪声特性。
2.
Then the robot poses and map information are updated synchronously with the help of extended Kalman filter(EKF).
该方法提取颜色区域作为视觉路标;在分析全景视觉成像原理和定位不确定性的基础上建立起系统的观测模型,定位出路标位置,进而通过扩展卡尔曼滤波算法(EKF)同步更新机器人位置和地图信息。
3.
With the aim of solving the low positioning accuracy and low robustness problems of vision SLAM algorithm,Extended Kalman Filter(EKF) method based on binocular vision and odometer is proposed in this paper.
针对视觉SLAM要解决的定位精度低和鲁棒性低的问题,提出一种基于双目视觉传感器与里程计信息的扩展卡尔曼滤波SLAM方法,应用改进的SIFT算子提取双目视觉图像的环境特征获得特征点,并构建出视觉特征地图;应用扩展卡尔曼滤波算法融合视觉信息与机器人位姿信息,完成同时定位与地图创建。
补充资料：卡尔曼滤波
见波形估计。
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