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。
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.
同时利用卡尔曼滤波误差方程对自主导航算法进行误差分析，并将两种分析结果作比较。
At the same time, we use Kalman filter error equations in errors analysis for autonomous navigation algorithm, and compare the analysis results of the two methods.
采用历元状态滤波建立了星上自主中长期轨道预报方法，并以太阳同步轨道卫星为例对算法进行了仿真验证。
The on-board autonomous term orbit prediction is built according to epoch state filter. And sun-synchronous orbit satellites are taken as examples for the simulation.
用UD分解改进EKF粒子滤波算法，并将其应用于基于星光仰角测量的探测器自主导航方案。
Using UD decomposing to modify EKF Particle filter was imported into the navigation scheme based on the measurement of elevation Angle of star.
利用UKF滤波定轨算法，明显提高了自主定轨的精度。
Based on UKF the precision of autonomous navigation was improved obviously.
本文研究了基于UK F滤波器的自主相对导航算法。
This paper focuses on the autonomous relative guidance algorithm based on the UKF in the process of proximity.
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.
给出了基于两步递推滤波的车辆自主式导航系统模型及其航位递推算法。
The model of vehicular autonomic navigation system and dead reckoning (DR) algorithm based on two-step recursive filtering are proposed in this paper.
给出了基于两步递推滤波的车辆自主式导航系统模型及其航位递推算法。
The model of vehicular autonomic navigation system and dead reckoning (DR) algorithm based on two-step recursive filtering are proposed in this paper.
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