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.
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