The application of Kalman filter in potential titration analysis is studied.
本文研究卡尔曼滤波在电位滴定分析法中的应用。
After performing Kalman filter, the optimal state estimation can be obtained.
进行卡尔曼滤波后，可以获得系统状态最优估计值。
The paper provides a new algorithm by introducing GM (1, 1) into Kalman filter .
针对这一问题，提出了一种基于GM （1， 1）模型的跟踪卡尔曼滤波方法。
With the algorithm of extend Kalman filter, the target motion analysis is discussed.
运用扩展卡尔曼滤波算法，研究了该系统的目标运动分析问题。
A method for estimating trajectory parameters by Kalman filter is introduced in this paper.
介绍了利用卡尔曼滤波器估计弹道参数进而外推出敌方炮位的方法。
Kalman filter is used to predict and track markers, making the tracking of markers more veracious.
利用卡尔曼滤波算法进行标记点预测和跟踪，提高了跟踪的准确性。
Due to the nonlinearity of the state and measurement equations, the extended Kalman filter is used.
由于状态和观测方程都是非线性的，故采用了扩展的卡尔曼滤波器。
In tracking aspect, use carrier tracking loop based on Kalman filter to improve tracking performance.
在跟踪方面，采用了基于卡尔曼滤波的载波跟踪环来提高跟踪环的性能。
A small target detection method using Kalman filter as the clutter background prediction was presented.
提出了一种采用卡尔曼滤波器作为杂波背景预测器的小目标检测方法。
The traditional method of Kalman filter for passive sonar target track association is limited in practice.
传统的目标航迹关联方法卡尔曼滤波法在实际应用时有局限性。
This paper investigates the application of adaptive Kalman filter in Moving Satellite Communication System.
研究了自适应卡尔曼滤波技术在移动卫星通讯系统中的应用。
The two-stage Kalman filter is excellent than standard Kalman filter in the presence of unknown random bias.
当动力学模型存在未知的随机系统偏差时，两阶段卡尔曼滤波要优于标准卡尔曼滤波。
The kalman filter algorithm under hybrid coordinate and unscented transformation (UT) algorithm are investigated.
研究了混合坐标系下的卡尔曼滤波算法和采样变换(UT)算法。
This paper introduces and analyzes the application of the least square method and Kalman filter in time prediction.
介绍和分析了最小二乘和卡尔曼滤波方法在时间预报中的应用。
The temporal effects , when appropriately modeled , can be taken into account by means of the kalman filter theory.
此一时间上的相关性经过适当的模式化后，可借由卡曼滤波器的理论将其纳入。
In this thesis, the specific parameter estimating by Kalman filter method was investigated under actual construction.
本文着重研究了在实际施工中，利用卡尔曼滤波法进行参数估计的具体做法。
This paper presents the main idea of adaptive observation and ensemble transform Kalman filter and their development.
给出适应性观测理论和集合变换卡尔曼滤波方法及其研究现状的综述。
This paper introduces an algorithm of adaptive Kalman filter and its microcomputer software for extracting EEG signal.
本文介绍了一种提取脑电信号的自适应卡尔曼滤波算法及其微机处理软件。
An approach is proposed that this deviation can be obtained by using the weld pool image centroid and a Kalman filter.
为此提出一种利用熔池图像质心和卡尔曼滤波来间接获取电弧与焊缝偏差的方法。
Utilizing the wheel speed signals and applying the Kalman filter technology, the wheel angular acceleration is calculated.
利用车轮角速度信号，应用卡尔曼滤波技术来计算车轮角加速度信号。
The reason that the residual test method based on Kalman filter is insensitive to sensor soft fault detection is analyzed.
分析了基于卡尔曼滤波器的残差检验法对传感器缓变故障检测的不敏感性原因。
Extended Kalman Filter (EKF) and converted measurement Kalman Filter (CMKF) have been widely used in radar target tracking.
在雷达目标跟踪中，扩展卡尔曼滤波(ekf)和转换坐标卡尔曼滤波(CMKF)得到了广泛的应用。
This paper discusses mainly the technique of parameter hybrid of Kalman filter applied in the system of relative navigation.
本论文主要是研究卡尔曼滤波参数混合技术应用于民用飞机相对导航系统。
Kalman filter used in linear discrete stochastic system has good convergence and the ability to remove high frequency noises.
卡尔曼滤波用于线性离散随机系统具有非常好的收敛性和滤除高频噪声的能力。
In the tracking stage, track face using kalman filter and skin-color feature, if fail to track then turn into detecting stage.
跟踪阶段用卡尔曼滤波器结合肤色特征跟踪人脸，如果跟踪失败，转入检测阶段。
Kalman filter is a linear minimum variance state estimator, and it combined array antenna and multiuser detection effectively.
卡尔曼滤波是一种线性最小方差状态估计，把它有效地结合阵列天线与多用户检测。
The basic variations in Kalman filter architecture are discussed and the multiple model estimation algorithm is presented as well.
讨论了建立卡尔曼滤波器的基本变量，最后探讨了卡尔曼滤波的多模型估计算法。
New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, and the Kalman filter.
新的章节，也增加了在黄木樨草分解，偏自相关，长期记忆过程，和卡尔曼滤波。
The whole algorithm conformation is very skilled. Because the number of kalman filter is lesser, algorithm is real-time, and robust.
算法构造巧妙，由于使用了较少的卡尔曼滤波器，算法实时性好，鲁棒性更好。
The Successive Orthogonalization Decentralized Kalman Filter (SODKF ) is a new method which is used for large system state estimation.
逐次正交化分布式卡尔曼滤波器是对大系统进行状态估计的一种新方法。
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