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Motion Prediction of Non-Cooperative Target based on Autoregressive Model

机译：基于自回归模型的非合作目标运动预测

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This paper describes a framework for predicting future positions and orientation of non-cooperative targets in the complex space environment employing autoregressive model (ARM). No constraints are placed on the target motion. Trajectories of the non-cooperative targets are not known a priori, but we assume that previous knowledge about position and orientation are available to the ARM form sensory devices. Based on the historical knowledge of the target motion, the parameters of the ARM are obtained with conditional maximum likelihood estimation. Once the parameters of the ARM are determined, the (n + n_p)th (n_p is the prediction horizon) position and orientation of a non-cooperative target could be calculated in a truly dynamic sense based on its first n positions and orientations. Simulation results show the feasibility and performance of the proposed scheme when compared with the motion prediction using truly dynamic model. The predication model will be an essential part in designing a trajectory planning algorithm for a space robot executing on-orbit capture activities.
机译：本文介绍了一种使用自回归模型（ARM）预测复杂空间环境中非合作目标未来位置和方向的框架。目标运动没有任何限制。非合作目标的轨迹不是先验的，但是我们假设先前有关位置和方向的知识可用于ARM形式的感觉设备。基于目标运动的历史知识，可以通过条件最大似然估计获得ARM的参数。一旦确定了ARM的参数，就可以基于其前n个位置和方向以真正的动态意义计算出非合作目标的第（n + n_p）个（n_p是预测范围）的位置和方向。仿真结果表明，与采用真实动态模型的运动预测相比，该方案具有可行性和性能。预测模型将是为执行在轨捕获活动的空间机器人设计轨迹规划算法的重要组成部分。

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