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。
With the algorithm of extend Kalman filter, the target motion analysis is discussed.
推广了线性卡尔曼滤波算法，导出雷达混合坐标系下的扩展卡尔曼滤波算法；
The linear kalman filtering algorithm is extended under the mix coordinate of radar;
为了提高定位精度，本文又引入扩展卡尔曼滤波算法对原始定位结果进行处理。
For improving the locating accuracy, the EKF algorithm is introduced to process the initial locating results.
针对多基地雷达系统跟踪近距离高加速机动目标的场合，提出了一种并行扩展卡尔曼滤波算法。
To investigate the problem of tracking a high accelerating maneuvering target with netted radar system, the parallel extended Kalman filtering algorithm is derived.
目前，实现定位跟踪的算法有很多，如卡尔曼滤波算法、扩展卡尔曼滤波算法、粒子滤波算法等。
At present, there are many algorithms to achieve position tracking, such as the Kalman filter algorithm, extended Kalman filter algorithm, particle filter algorithm and so on.
最后，给出了扩展卡尔曼滤波算法、无迹卡尔曼滤波算法和粒子滤波算法的推导过程和仿真分析。
Finally, the computational procedures and simulation analysis of extended Kalman filtering algorithm, unscented Kalman filtering algorithm and particle filtering algorithm is presented.
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。
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.
利用扩展卡尔曼滤波算法估计机械手各关节的初始角位置，从而间接地保证机械手在工作空间内的绝对定位精度。
The initial angular positions of the joints are estimated by the extended Kalman filter algorithm, then the manipulator's absolute locating accuracy in its workspace is guaranteed indirectly.
仿真结果表明变周期距离参数化扩展卡尔曼滤波算法能有效解决经典扩展卡尔曼滤波算法在纯方位角目标跟踪时可能出现的滤波发散现象，并能处理声音信号的传输时间延迟问题。
The simulation results indicate that the variable cycle RP-EKF algorithm can resolve the filtering instability of EKF for the bearing-only target tracking and the time-delay problem.
设计了一种蜂窝系统无线定位中使用的扩展卡尔曼滤波(ekf)算法，对其性能进行了仿真。
An extended Kalman filtering (EKF) algorithm is designed for radiolocation in cellular communication system, simulation is conducted to examine its performance.
在建立目标机动模型与测量方程的基础上，运用修正增益扩展卡尔曼滤波（MGEKF）算法，实现对机动目标进行定位与跟踪。
On the basis of building target movement model and measuring equation, applying MGEKF algorithm, realizes the locating and tracking for mobile target.
设计扩展卡尔曼滤波器进行卫星编队轨道状态估计，数学仿真结果验证了这种导航方案和算法的有效性。
The orbit states estimation is achieved through the extended Kalman filters design. The simulation results verify the validity of this navigation method, and show preferable navigation accuracy.
根据UKF和扩展卡尔曼滤波(ekf)计算过程相似的特点，设计了SSUKF和EKF相结合的混合卡尔曼滤波算法。
According to the similar computation process of UKF and extended Kalman filter (EKF), the combined Kalman filter based on SSUKF and EKF was designed.
本文提出了一种利用扩展卡尔曼滤波器算法来估计BLDCM的转速和位置的方法。
A novel method for speed and rotor position estimation of BLDCM, which applies extend Kalman filter (EKF), is presented in this paper.
无轨迹卡尔曼滤波器(ukf)作为扩展卡尔曼滤波器(ekf)的进化算法在许多非线性估计问题上取得了成功的应用。
Unscented Kalman Filter (UKF), which is an evolutional algorithm of Extended Kalman Filter (EKF), has been successfully applied in many nonlinear estimation problems.
模块的组成、功能，详细说明了姿态角的计算、初始对准以及采用的稳定、实时、变增益自适应扩展卡尔曼滤波估计算法。
Attitude calculation techniques, initial alignment methods and a real-time adaptive extended Kalman filter used for improve the system precision were discussed.
在确定卫星姿态确定的状态估计法中，经典的扩展卡尔曼滤波(ekf)和新提出的非线性预测滤波(NPF)这两种实时滤波算法各有优缺点。
In state estimation of satellite attitude determination, both traditional extended Kalman filter (EKF) and the proposed nonlinear predictive filter (NPF) have their own merits and defects.
为改善多基地雷达系统对高机动目标的跟踪性能，提出了基于自适应高斯模型和扩展卡尔曼滤波(ekf)的机动目标跟踪算法。
Maneuvering target tracking algorithm based on adaptive Gauss model and EKF was built for improving the tracking performance in Multi-static systems.
本文将方位角变化率测量信息也引入该定位问题，提出了基于MGEKF（修正增益扩展卡尔曼滤波），对三维运动辐射源的无源定位跟踪算法。
In this paper, the azimuth angle changing rate measurement is introduced into this passive location problem and a location algorithm for 3-D moving emitters based on MGEKF is given.
对经典的卡尔曼滤波以及针对非线性系统的扩展卡尔曼滤波，不敏卡尔曼滤波算法进行了分析比较。
The state estimations algorithm for Target tracking have been studied and compared such as Kalman filter, Extented Kalman filter and Unscented Kalman filter.
针对对准模型的特性，推导了SPKF简化算法，进行了静基座下基于扩展卡尔曼滤波(ekf)、简化spkf的SINS初始对准仿真。
According to the peculiarity of the SINS nonlinear error model, the simplified SPKF algorithm was derived, and the SINS initial alignment were simulated based on EKF and simplified SPKF.
针对对准模型的特性，推导了SPKF简化算法，进行了静基座下基于扩展卡尔曼滤波(ekf)、简化spkf的SINS初始对准仿真。
According to the peculiarity of the SINS nonlinear error model, the simplified SPKF algorithm was derived, and the SINS initial alignment were simulated based on EKF and simplified SPKF.
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