1) suboptimal strong tracking filter
次优强跟踪滤波器
1.
According to the approximation error upper bound of the deduced predictive model, based on the multiple fading extended Kalman filter a method of design the suboptimal strong tracking filter was pro.
针对大型聚乙烯工业装置质量指标实时估计的复杂性,基于乙烯聚合原理推导了大型聚乙烯工业装置质量指标实时预测模型,提出了一种次优强跟踪滤波器设计方法用于根据实验室分析数据反馈修正模型预测并实时估计质量指标。
2) strong tracking filter
强跟踪滤波器
1.
Jerk Model and Adaptive Maneuvering Target Tracking Algorithm Based on Strong Tracking Filter;
基于强跟踪滤波器的Jerk模型目标跟踪算法
2.
Application of strong tracking filter in passive target tracking;
强跟踪滤波器在被动跟踪中的应用
3.
Detection and Diagnosis of Failures Based on Interactive Multiple-Model Strong tracking filter;
基于交互多模型强跟踪滤波器的故障检诊方法
3) strong tracking Kalman filter
强跟踪Kalman滤波器
1.
The navigation accuracy and reliability of a strap down inertial navigation system (SINS)/global positioning system (GPS) integrated navigation system were improved using a fault detection method with a strong tracking Kalman filter.
将强跟踪Kalman滤波器用于该导航系统的故障检测中 ,并提出了一种自适应调节的带多重次优渐消因子的 Kalman滤波器 ,给出了仿真结果。
4) Strong tracking filter
强跟踪滤波
1.
Identification of non-linear system parameters based on strong tracking filter;
基于强跟踪滤波器的非线性系统参数辨识方法
2.
Vehicle nonlinear state estimation based on strong tracking filter
基于强跟踪滤波的车辆非线性状态估计
3.
This dissertation puts forward a method of designing the suboptimal strong tracking filter, which is based on the approximation error upper bound of nonlinear separation model and applied to design feedback corrected controller in predictive control.
本文提出了_种基于非线性分离模型逼近偏差上限的次优强跟踪滤波器设计方法，运用到预测控制中反馈校正的设计。
5) Strong tracking filtering
强跟踪滤波
1.
In this paper,the theory of Strong tracking filtering(STF) is applied in the object prediction of autonomous robots to avoid the disadvantages of other methods by introducing fading factors.
本文提出将强跟踪滤波理论应用于全自主机器人目标预测,通过引入渐消因子,克服了其它目标预测方法的缺点。
2.
This research paper employs the Kalman filtering and the strong tracking filtering in the optimal estimation theory to dynamically estimate Chinese listed companies permanent earnings,which are critical for stock market investment decision.
本文应用最优估计理论中的卡尔曼滤波和强跟踪滤波方法对上市公司的永久性盈余进行动态估计。
6) STF
强跟踪滤波
1.
According to the model assumption,the parameters of the fault trend process can be obtained by using STF(strong tracing filter).
在测量变量受到平稳噪声干扰的情况下,首先依据对测量数据的统计检验判断出故障过程,然后根据对故障过程的先验知识,利用强跟踪滤波器辨识指数趋势项的参数,同时对建模误差进行ARMA时序分析,最后结合趋势项和时序预测给出故障趋势的总体预测。
2.
Therefore,it is proposed to use Strong Tracking Filter(STF) in maneuvering target tracking.
基于此,将强跟踪滤波运用到机动目标跟踪上。
3.
Comparing the performance of Kalman with STF,it is showed this method is with high accuracy,high anti-jamming and strong tracking ability.
针对该问题,提出并设计了应用强跟踪滤波技术的捷联惯导系统动基座初始对准方案。
补充资料：输出反馈(见线性二次型次优控制)
输出反馈(见线性二次型次优控制)
output feedback
3h日c卜口fon以以{输出反馈型次优控制。(output王eedbaek)见线性二次
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条