1) strong tracking
强跟踪
1.
According to the relative position between the interceptor and the maneuvering target,using seeker spherical surface model,the paper gives out the line-of-sight (LOS) rate for guidance through strong tracking adaptive estimation based on current statistics model.
依据拦截弹与机动目标间的相对位置关系,采用导引头球面模型,基于当前统计模型,实现了强跟踪状态自适应滤波,计算出了可用于制导的视线角速率。
2.
Aiming at the problem that ordinary UKF is slow and has steady bias in identifying the parameter bias fault of nonlinear system,the strong tracking UKF theory is proposed in this paper.
本文针对普通UKF辨识非线性系统故障时跟踪缓慢且存在稳态偏差的问题,且为了抑制模型误差的影响,基于正交性原理,定义了实现强跟踪需满足的2个条件,通过强制使得残差序列保持近似高斯白噪声,得到了各状态渐消因子的求解方法,进而提出了强跟踪UKF方法。
3.
A strong tracking state filtering algorithm with a singular fading factor is proposed for a class of nonlinear systems with multiplicative noise.
针对带乘性噪声的一类非线性系统,给出了1种带单重渐消因子的强跟踪状态滤波算法。
2) strong tracker
强跟踪器
1.
An improving information fusion approach based on strong tracker adaptive filtering is proposed.
给出了改进的强跟踪器自适应滤波信息融合方法,该方法信息融合结果精度高,同时对突变信号有很强的实时跟踪能力。
3) strong shadowing property
强跟踪性
1.
The strong shadowing property for a continuous map f on a compact metricspace X is investigated.
研究了紧度量空间上的连续映射的强跟踪性,证明了:提升系统(~X,~f)具有强跟踪性的充要条件是(X,f)具有强跟踪性;若f满足Lipschitz条件,则f具有强跟踪性当且仅当对任意k∈Z+,fk具有强跟踪性。
2.
In this paper, the notion of the strong chain recurrent set is introduced to study the properties of the strong shadowing property.
为了研究强跟踪性 ,本文给出了强链回归集的定义 。
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：椐椐强强
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