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1)  SK strong tracking filter
SK 强跟踪滤波
1.
Especially SK strong tracking filter and Sage-Husa adaptive filter are investigated in derail.
针对 Kalman 滤波发散这一工程现象,分析了造成 Kalman 滤波发散的原因和控制 Kalman 滤波发散的机理;重点研究了 SK 强跟踪滤波方法和 Sage-Husa 自适应滤波算法,提出了 SK 强跟踪滤波不发散的自适应遗忘因子的新算法;分析了强跟踪条件下量测噪声阵遗忘因子权重变化的物理意义;推导并指出了 Sage-Husa 自适应滤波算法的局限性。
2)  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。
3)  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。
4)  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。
5)  SK peak
SK峰
1.
The study on the aging of the drawn pearlite steel by internal friction measurment at 473K shows that the relaxation strength of the snoek peak increases firstly and then decreases within the increment of aging time,while the relaxation strength of the SK peak decreases all the time and the width of the peak decreases too.
通过内耗对473 K时效的拉拔珠光体钢研究表明,随着保温时间增加,Snoek峰驰豫强度先增后降,而SK峰驰豫强度不断降低,且峰宽变窄。
6)  sk-buff
sk buff
补充资料:椐椐强强
1.相随貌。
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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