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.
提出一种估计异步电机转子速度和转子磁链的新型降阶推广卡尔曼滤波器算法，建立了基于此算法的异步电机无速度传感器矢量控制系统。
A speed sensorless vector control system of induction motor with estimated rotor speed and rotor flux using a new reduced order extended Kalman filter is proposed.
为了有效解决运动目标遮挡时目标信息容易丢失从而导致跟踪失败的问题，提出一种基于卡尔曼滤波器的运动目标跟踪算法。
In order to effectively solve the problem that the loss of object information under occlusion causes the failure of tracking, moving objects tracking algorithm is presented based on Kalman filter.
本文依据卡尔曼滤波器在使用最佳增益时，其余差序列互不相关的性质，开发了一种新的渐消滤波算法。
A new fading filtering algorithm is developed based on the property of Kalman filter that the sequence of residuals is uncorrelated when the optimal gain is used.
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 whole algorithm conformation is very skilled. Because the number of kalman filter is lesser, algorithm is real-time, and robust.
用非线性车辆模型线性化方法，设计了基于广义卡尔曼滤波器和广义龙贝格观测器的质心侧偏角估计算法。
Through linearizing nonlinear vehicle model, vehicle side-slip Angle estimation algorithms based on generalized Kalman filter and generalized Luenberger observer are formulated.
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.
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。
The basic variations in Kalman filter architecture are discussed and the multiple model estimation algorithm is presented as well.
在以常规卡尔曼滤波器为基础的各种跟踪算法中，要求精确的模型和噪声统计，但在实际问题中，大多数情况上述要求不能满足。
Accurate models and noise statistics are required in many tracking algorithms based on the traditional Kalman filter, which are difficult to be satisfied in engineering application.
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.
针对当前自适应组合导航系统算法的研究趋势，总结了卡尔曼滤波技术的缺陷和利用智能融合技术提高滤波器性能的设计思想。
The adaptive Kalman filtering (AKF) based on intelligent information fusion algorithm has currently became an effective approach to enhance the integrated navigation system's robustness and accuracy.
针对卡尔曼滤波器对系统模型依赖性强、鲁棒性差和跟踪机动目标能力有限的问题，提出了一种新的利用混合模糊逻辑和标准卡尔曼滤波器的联合算法。
The Kalman filter has been commonly used in target tracking, however its performance may be degraded in presence of maneuver, low robustness and strong model dependence.
针对卡尔曼滤波器对系统模型依赖性强、鲁棒性差和跟踪机动目标能力有限的问题，提出了一种新的利用混合模糊逻辑和标准卡尔曼滤波器的联合算法。
The Kalman filter has been commonly used in target tracking, however its performance may be degraded in presence of maneuver, low robustness and strong model dependence.
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