By analyzing the relationship of the Kalman filtering model errors and the forecasting residual vector, a method of detecting gross errors was proposed.
通过分析卡尔曼滤波模型误差与预测残差向量之间的关系，提出了对粗差进行探测的方法，并通过一个实例说明了该方法的有效性。
Based on the adaptive Kalman filtering algorithm, an estimator for the GPS PN code tracking error with model bias is proposed.
在自适应卡尔曼滤波算法的基础上提出了一种带模型偏差的GPS伪码跟踪误差估计器。
A simulation model of the method is set up using filtering algorithm of extend Kalman, and the system simulation is made using simulative data at the basic of a synchronization satellite of the earth.
结合推广的卡尔曼滤波算法，建立了该方法的仿真模型，以某地球同步卫星为背景，利用模拟数据进行了系统仿真。
Kalman filter model of oil pipeline pressure wave is given out and the influences of model parameters on filtering effect are analysed.
给出了输油管道压力信号的卡尔曼滤波模型，并分析了模型参数对滤波效果的影响。
Considering the radiation problem of filtering resulting from the inaccurate model of the Kalman filter, data fusion algorithm based on the fuzzy Kalman filter is advanced.
并针对卡尔曼滤波因模型不准确而导致的滤波发散问题，提出了模糊卡尔曼的数据融合算法。
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。
Based on the algorithm of maneuvering acceleration current statistical model adaptive filtering, the adaptive kalman filtering algorithm based on QR matrix decomposition is presented in this paper.
在机动加速度“当前”统计自适应卡尔曼滤波算法的基础上，引入了基于Q - R矩阵分解的自适应卡尔曼滤波算法。
The sequential regression analysis was adopted to screen off the secondary factors, and the Kalman filtering technique was used to estimate innovation coefficients of the model dynamically.
通过逐步回归分析方法剔除次要影响因素，并采用卡尔曼滤波方法动态预测回归残差项。
First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm.
首先，利用在线模糊竞争学习方法划分输入变量的模糊输入空间，然后利用卡尔曼滤波算法估计模糊模型的参数。
The extended Kalman filtering method has been effectively used in the nonlinear model.
扩展的卡尔曼滤波方法已经有效地用于非线性模型。
The input space of fuzzy system is partitioned by means of real time recursive fuzzy clustering, and the parameters of fuzzy model are confirmed by Kalman filtering.
利用递推模糊聚类算法实时对系统的输入空间进行模糊划分，利用卡尔曼滤波算法确定参数。
At last the normal Kalman filtering algorithm model was given and the deducing process of this algorithm was described in detail.
最后，给出了标准的卡尔曼滤波算法模型并详细描述了算法的推导过程。
The paper also presents the applications of the model to computer simulation, computer supervisor control and on-line state estimation by state observer and Kalman filtering respectively.
本文同时介绍了数学模型在实现计算机模拟、计算机监督控制和状态观测器、卡尔曼滤波器等方面的应用。
In this paper an extended Kalman-filtering was used to improve the precision of the simulation of mathematical model.
为了提高数学模型的模拟精度，本文采用广义卡尔曼滤波技术进行递推滤波估计。
In this paper an extended Kalman-filtering was used to improve the precision of the simulation of mathematical model.
为了提高数学模型的模拟精度，本文采用广义卡尔曼滤波技术进行递推滤波估计。
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