Space debris modelling and situational awareness

空间目标的姿态状态在定轨和轨道传播中一直被忽视。其结果是物体位置的不确定性。航天工业正在采取一项举措，提高我们的跟踪能力，以提高已知和预测空间物体位置的准确性。改变态度将是实现这一目标的一种方式。此外，碎片建模研究的结果表明，碰撞有可能导致低地球轨道碎片数量的增加。主动清除碎片（ADR），通常是通过物理接口和离轨机动，已被建议作为减缓这种增长的一种技术。姿态状态信息对于ADR使命选择合适的物体移除和执行使命本身都至关重要，确定非活动空间物体的姿态状态需要外部观测数据，通常是雷达或光学观测数据。在所有可用的数据类型中，没有一种比opticallight curve更常见。因此，本研究的目标是开发从光学光变曲线数据中获取空间物体姿态状态信息的技术，根据该领域正在进行的研究，所选择的方法是将问题分为正向模型和反向模型。正演模型负责生成准确的合成光变曲线。然后，逆模型将使用参数优化技术来定义可能的姿态状态解。

The attitude states of space objects have long been neglected in orbital determination and propa-gation. The result of this is uncertainty in the positions of objects. There is an ongoing initiativewithin the space industry to improve our tracking capabilities in order to improve the accuracywith which the positions of space objects are known and can be predicted. Incorporating attitudestate would be one way of achieving this goal. Additionally, the results of debris modelling research has indicated a possibility of a collision driven growth in the low-Earth orbit debris population. Active Debris Removal (ADR), typically though a physical interface and de-orbit manoeuvre, has been suggested as a technique for mitigatingthis growth. Attitude state information is of the utmost importance to an ADR mission both forselecting a suitable object to remove and for carrying out the mission itself.Determining the attitude state of an inactive space object requires external observation data,typically radar or optical. Of all the available data types, none are more common than opticallight curves. It is therefore the objective of this research to develop techniques to derive informationon space object attitude state from optical light curve data.In light of the going research in the field, the selected approach was to split the problem into aforward model, and an inverse model. The forward model is responsible for generating accuratesynthetic light curves. The inverse model will then use parameter optimisation techniques to define possible attitude state solutions.