提出了一种模糊自适应卡尔曼滤波算法。
An algorithm of adaptive fuzzy Kalman filtering is presented.
为了解决常规卡尔曼滤波法存在的不足，给出了用模糊推理系统与卡尔曼法相结合的方法。
In order to resolve the shortcomings of the traditional Kalman filtering, a new method is presented in which the fuzzy reasoning system is combined with the traditional Kalman technology.
提出了一种基于模糊聚类和卡尔曼滤波的多运动目标检测的技术，并将其应用于车辆的检测与跟踪。
A kind of technique for detection of multiple moving objects based on fussy clustering and Kalman filtering was brought forward, and has been applied to vehicle detection and tracking.
提出了一种利用模糊逻辑控制器来在线调节卡尔曼滤波器的自适应数据融合方法，并着重研究了其在GPS/INS组合导航中的应用。
This paper presents an adaptive data fusion method which uses a fuzzy logic controller to online adjust the Kalman filter, and focuses on its application to the integrated GPS/INS navigation.
提出了一种新的基于遗传模糊软分类和卡尔曼滤波方法的模糊辨识算法。
A method of fuzzy identification based on genetic soft fuzzy clustering and Kalman filtering method is proposed.
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。
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.
对各方程序卡尔曼滤波，通过T -SFIMMA算法进行基于T - S模糊模型的自适应卡尔曼滤波计算，实现系统模型的实时跟踪与自动转换。
Kalman filter of the kind of equations was calculated with T-SFIMMA algorithm based on adaptive Kalman filter algorithm of T-S fuzzy model, realize the tracking and automatic switchover of models.
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。
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.
再以该近似解和协方差矩阵为初值，由无迹卡尔曼滤波(ukf)实时估计双差整周模糊度的精确解。
Then the float ambiguity resolution was passed into an unscented Kalman filter as initial state value. UKF estimated the precise ambiguity resolution in real time with the initial value.
针对这一问题，提出了利用惯性空间中地球重力加速度信息的捷联惯导自主粗对准方法，以及基于模糊自适应卡尔曼滤波的自主精对准方法。
The paper proposes a new alignment scheme, in which the gravity message is used in the coarse alignment procedure, and an adaptive Kalman filter is used in the fine alignment procedure.
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。
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.
为了解决常规卡尔曼滤波法存在的不足，给出了用模糊推理系统与卡尔曼法相结合的方法。
In order to resolve the shortcoming of the traditional federal Kalman filtering, a new method is presented in which the fuzzy reasoning system is combined with the traditional Kalman technology.
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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