The position and speed feedback signal estimated is generated by EKF.
由ekf产生位置及速度反馈信号估计值。
At last, we also discuss the further research directions of the P-EKF.
最后，本文对P -EKF算法的研究方向进行了讨论。
The performance of the REKF is illustrated in comparison with the standard EKF.
对REKF和传统扩展卡尔曼滤波(ekf)的性能进行对比。
However, the linearization of EKF causes error which influences the accuracy of the system.
但是EKF的线性化会带来截断误差，从而影响系统定位精度。
SLAM is completed by fusing the information of SIFT features and robot information with EKF.
应用扩展卡尔曼滤波器融合SIFT特征信息与机器人位姿信息完成SLAM。
The comparison experiment with IMM based on EKF, IMMPF demonstrates the validity of the two algorithms.
最后通过和一般基于EKF的IMM算法、IMMPF算法的比较，验证了这两个算法的有效性。
For improving the locating accuracy, the EKF algorithm is introduced to process the initial locating results.
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。
The dissertation researches on the PMSM vector control system using Extended Kalman Filtering (EKF) algorithm.
本文是对永磁同步电机的扩展卡尔曼无位置算法（EKF）的矢量控制系统的研究。
To the pole orbit micro-satellite, this paper provides a modified EKF arithmetic based on magnetometer individually.
针对极轨微小卫星，提出了一种基于磁强计的改进EKF算法。
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。
The extended Kalman filter(EKF) and converted measurement Kalman filter(CMKF) have been widely used in radar target tracking.
在非线性量测的情况下，EKF和CMKF得到了广泛的应用。
The focus of this dissertation is in chapter three- application of EKF estimator in speed-sonserless vector control system of IM.
本论文的核心是第三章——基于卡尔曼滤波估计的无速度传感器矢量控制系统。
A novel method for speed and rotor position estimation of BLDCM, which applies extend Kalman filter (EKF), is presented in this paper.
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。
When the estimated error of gyro drift reduces to some low extent, the filter was switched to the fusion mode of EKF and optimal REQUEST.
当陀螺漂移误差减小到一定程度，再切换为EKF与最优REQUEST算法融合的双重滤波器。
Using UD decomposing to modify EKF Particle filter was imported into the navigation scheme based on the measurement of elevation Angle of star.
用UD分解改进EKF粒子滤波算法，并将其应用于基于星光仰角测量的探测器自主导航方案。
The theoretical analysis is verified and improved by comparing the results of FEM calculation and EKF estimation with experimental measurement.
综合泄漏流量的有限元计算结果与扩展卡尔曼滤波器的估计结果，与实测结果相比较，进一步完善理论分析方法。
Finally, we implemente our EKF-based RSS localization scheme to verify its computational efficiency and estimation accuracy in a real environment.
最后，我们在现场环境下验证了本文提出的基于接收信号强度均值的无线传感器网络定位方案的计算效率和定位性能。
The tracking system described above has a very strong non-linearity, so the classical linear filters, such as EKF, will not get a good performance.
但是，上面提出的跟踪系统具有很强的非线性，应用经典的线性滤波方法，比如ekf，难以得到令人满意的结果。
According to the similar computation process of UKF and extended Kalman filter (EKF), the combined Kalman filter based on SSUKF and EKF was designed.
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。
Maneuvering target tracking algorithm based on adaptive Gauss model and EKF was built for improving the tracking performance in Multi-static systems.
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。
By using the forming filter and EKF, the precision of states estimation is increased and a effective estimation of stochastic sea interference is performed.
通过引入成型滤波器，采用EKF，提高了状态估计的精度，实现对随机海浪扰动力和力矩的估计。
The estimated results of the slopes are introduced into the EKF to get state estimations without radome interference which generates the guidance law command.
将斜率估计结果代入EKF，得到滤除天线罩误差影响的系统状态量估计结果并形成制导指令。
An extended Kalman filtering (EKF) algorithm is designed for radiolocation in cellular communication system, simulation is conducted to examine its performance.
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。
Unscented Kalman Filter (UKF), which is an evolutional algorithm of Extended Kalman Filter (EKF), has been successfully applied in many nonlinear estimation problems.
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。
The new design avoids the linearization of the nonlinear system equations, and has higher theoretical accuracy and stability than the EKF-based or EKF-like algorithms.
与基于EKF的方法相比，在同等计算复杂度下，该算法具有更高的精度和更好的稳定性。
The new method can give more accurate results of parameter estimation with more effective con-vergence rate than EKF does since the computation of innovation is improved.
由于改善了偏差估计的新息计算，使得偏差估计的结果更准确、收敛速度更快。
This paper presents a hybrid model for urban arterial travel time prediction based on the so-called state space neural networks (SSNN) and the extended Kalman Filter (EKF).
提出了一种基于状态空间神经网络(SSNN)和拓展卡尔曼滤波(ekf)的混合式行程时间预测模型。
This paper presents a hybrid model for urban arterial travel time prediction based on the so-called state space neural networks (SSNN) and the extended Kalman Filter (EKF).
提出了一种基于状态空间神经网络(SSNN)和拓展卡尔曼滤波(ekf)的混合式行程时间预测模型。
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