Space debris modelling and situational awareness

长期以来，空间物体的姿态状态在轨道确定和轨道传播中一直被忽视。其结果是物体的位置不确定。空间工业内部正在开展一项举措，以改进我们的跟踪能力，以便提高知道和预测空间物体位置的精确度。将态度纳入其中将是实现这一目标的一种方式。此外，碎片模拟研究的结果表明，低地球轨道碎片数量有可能因碰撞而增长。主动碎片清除(ADR)，通常通过物理接口和脱轨操作，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任务来说是至关重要的，无论是对于选择要移除的合适对象还是对于执行任务本身都是至关重要的。确定非活动空间目标的姿态状态需要外部观测数据，通常是雷达或光学数据。在所有可用的数据类型中，没有一种比光学灯光曲线更常见。因此，本研究的目的是开发从光学光线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，选择的方法是将问题分解为正模型和逆模型。正向模型负责生成精确的合成光线曲线。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.