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首页> 外文期刊>Journal of Spacecraft and Rockets >Estimation and Prediction of Orbital Debris Reentry Trajectories
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Estimation and Prediction of Orbital Debris Reentry Trajectories

机译：轨道碎片折返轨迹的估计和预测

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摘要

Tracking and prediction of orbital debris trajectories have recently received a great deal of attention due to the increasing proliferation of such objects and the hazard they pose to operational spacecraft. Most analyses have focused on in-orbit dynamics, such as the probability of collision with low-orbiting satellites, especially the International Space Station. Less attention has been given to the problem of reentry estimation and impact prediction. During reentry, aerodynamic forces dominate the dynamics, which presents a challenge to the trajectory estimation problem because the relevant characteristics of debris (size, shape, and mass) needed to model the trajectory accurately are largely unknown. A ground-based trajectory estimation method is described that attempts to determine simultaneously the unknown time-varying ballistic coefficients with the state vector using an extended Kalman filter. This filter estimates the unknown ballistic coefficients by using dynamic process noise parameters based on an integral state model. A posteriori information from the filter is processed by a Monte Carlo algorithm to predict the impact location. Simulation results are presented and suggest a high degree of accuracy in both the estimation and prediction stages.
机译：由于这类物体的扩散不断增加以及它们对运行中的航天器造成的危害，轨道碎片轨迹的跟踪和预测最近受到了广泛的关注。大多数分析都集中在轨道动力学上，例如与低轨道卫星，特别是国际空间站发生碰撞的概率。对折返估计和影响预测的关注较少。在折返过程中，空气动力主导着动力学，这对轨迹估计问题提出了挑战，因为在很大程度上难以精确建模轨迹所需的相关碎片特征（大小，形状和质量）。描述了一种基于地面的轨迹估计方法，该方法试图使用扩展的卡尔曼滤波器同时确定状态向量的未知时变弹道系数。该滤波器通过使用基于积分状态模型的动态过程噪声参数来估计未知的弹道系数。来自过滤器的后验信息由蒙特卡洛算法处理，以预测碰撞位置。给出了仿真结果，并表明在估计和预测阶段都具有很高的准确性。

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