1) augmented Kalman filtering algorithrm
扩维卡尔曼滤波算法
2) extended Kalman filter
扩展的卡尔曼滤波算法
1.
The fusion algorithm based on extended Kalman filter in common use has the disadvantage of unsatisfactory tracking precision.
在实际系统中 ,常用的数据融合方法是基于扩展的卡尔曼滤波算法的融合算法 ,但是这种融合算法的跟踪精度并不是很高 。
4) VD-Kalman filtering algorithm
变维卡尔曼滤波算法
5) Kalman filter algorithm
卡尔曼滤波算法
1.
Therefore,we transform the rotational velocity of propeller to the speed of AUV,use the method of data fuse and presents a Kalman filter algorithm of AUV/SINS integrated navigation to reduce navigation error.
针对由于复杂海洋环境导致的DVL失效,使整个导航精度急剧下降的情况,该文将螺旋桨转速变换成AUV航速,利用航速惯导组合系统信息融合方法,提出了一种通过航速惯导组合导航的卡尔曼滤波算法,可以减小导航误差。
2.
Kalman filter algorithms for bearing-only target tracking are discussed.
本文介绍了纯方位目标运动分析的卡尔曼滤波算法,利用Lyapunov稳定性理论,通过计算算法的稳定性度量值,对三种卡尔曼滤波算法的稳定性进行了分析讨论,并通过仿真计算对各方法的估计性能进行了比较。
3.
Adaptive α-β filter algorithm was introduced,and then it was compared to LSR filter algorithm and Kalman filter algorithm,the mean square deviation of distance,position,speed and direction of these algorithms on the flight line was analyzed to discuss the specific use of the three filters in straight-line flight and about-sh.
介绍了一种改进的α-β滤波算法,然后将它和最小二乘滤波算法、卡尔曼滤波算法进行比较,对三种算法跟踪空中目标的直线飞行、改变航向的直线飞行两种情况的距离、方位、速度和航向等参数的均方差加以分析,证明了改进的α-β滤波算法在直线飞行模型下有着很好的综合效果,且计算量小,更有利于系统建模与仿真。
6) Kalman filtering algorithm
卡尔曼滤波算法
1.
Realization of kalman filtering algorithm of two dimensions in aerial information processing system;
空情处理系统中二维卡尔曼滤波算法及实现
2.
Through analysis on the running characteristics of the public transport vehicles,and then using Kalman filtering algorithm establish the prediction model for the travel time of the public transport vehicles.
通过分析公交车辆的行驶特性,利用卡尔曼滤波算法建立行程时间预测模型,并用该模型预测未来时段的公交车辆路段行程时间。
3.
In order to eliminate the effect of time delay of tactical digital information link(TADIL) on situation judgement in multifighter cooperative combat,we use a kind of Kalman filtering algorithm in polar coordinates to compensate for the error of time delay resulted from data link transmission.
为了解决多机协同空战中数据链延时对空战态势判断带来的影响,本文应用了一种极坐标系下的卡尔曼滤波算法,对数据链的传输延时误差进行补偿,并在此基础上进行目标运动参数的转换,计算工程技术人员感兴趣但机载测量设备无法测量的目标信息,比如目标进入角。
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