This paper presents an extended kalman filtering algorithm for tracking infrared target, using the outputs from a forward-looking infrared (FLIR) sensor as measuring value.
本文提出了一种直接把红外前视传感器的输出作为测量值来跟踪红外目标的扩展型卡尔曼滤波跟踪算法。
A fast Kalman filtering algorithm of netted radar system is presented for tracking a maneuvering target.
提出了一种快速卡尔曼滤波跟踪算法，用于组网雷达系统对机动目标的跟踪分析。
The quantized time delay method is adopted for comparison of short time intervals, and a Kalman filtering algorithm …
该系统采用了量化时延原理进行短时间间隔比对，对测量数据进行卡尔曼滤波处理。
Based on the adaptive Kalman filtering algorithm, an estimator for the GPS PN code tracking error with model bias is proposed.
在自适应卡尔曼滤波算法的基础上提出了一种带模型偏差的GPS伪码跟踪误差估计器。
At last the normal Kalman filtering algorithm model was given and the deducing process of this algorithm was described in detail.
最后，给出了标准的卡尔曼滤波算法模型并详细描述了算法的推导过程。
To investigate the problem of tracking a high accelerating maneuvering target with netted radar system, the parallel extended Kalman filtering algorithm is derived.
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。
The applications of the Kalman filtering algorithm in data processing (range and speed) of quasi continuous wave radar are studied. A new adaptive Kalman algorithm is realized.
主要完成卡尔曼滤波在准连续波雷达数据处理(距离和速度)中算法应用研究，实现一种新型自适应卡尔曼滤波算法。
Finally, the computational procedures and simulation analysis of extended Kalman filtering algorithm, unscented Kalman filtering algorithm and particle filtering algorithm is presented.
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。
The simulation results show that the adaptive Kalman filtering algorithm can track the maneuvering target, and it is characterized by high accuracy, good stability and quick convergence.
仿真结果表明，该算法可以较好地跟踪机动目标，具有精度高、稳定好、收敛快等特点。
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.
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.
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。
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