Quantifying the response of a synthetic light curve generation model to varying inputs.

摘要

Ensuring that operating within the near-Earth space environment does not become hampered by increasing numbers of Resident Space Objects (RSOs) is an important focus for the global space industry. Following research indicating a self-sustaining population of RSOs between 900 - 1000 km driven by collision activity, Active Debris Removal (ADR), the targeted removal of a RSO, was proposed as a possible reactive countermeasure. All of the most viable ADR proposals require accurate characterisation of the target object's motion both for target selection and for the removal manoeuvre. Another way of safeguarding near-Earth space is through improving upon current Space Situational Awareness (SSA) capabilities. Through better and more accurate positioning, a level of traffic control can be applied to space environment. This would allow for unwanted events to be identified in advance, and responded to more proactively. In either case, new techniques need to be developed for deriving information on object motion, from observation data. The attitude state of the object is of particular interest to both SSA and ADR. For SSA, the attitude of the object has a significant effect on the drag force, which is by far the largest force exerted on objects in Low Earth Orbit (LEO). As for ADR, the majority of proposals require a physical interface with the target object and as a result will rely heavily on attitude state characterisation. Of the available techniques for remote observation and measurement of space objects, optical measurements are by far the cheapest and simplest. As a result, large quantities of time-varying brightness data, on a range of active, inactive and unknown objects, has been collected. Hence developing techniques to derive information, such as attitude state, from light curves could be highly beneficial to ADR and SSA efforts. To examine the availability of attitude information in optical light curve data, a Synthetic Light Curve Forward Model (SLCFM) has been