基于稀疏光学数据的空间目标轨道确定
Orbit determination of space objects based on sparse optical data
摘要：
在建立地球轨道物体目录时，如果可用的光学观测很少，而不是对具体物体进行刻意的跟踪，如果没有以前对不同时间获得的观测进行相关，就不可能进行轨道确定。这个关联步骤是计算最密集的，并且随着要发现的对象的数量增加而变得越来越困难。本文测试了最近开发的两种不同的算法（以及相关的原型软件）来解决地球静止轨道（GEO）中物体的相关问题，包括用全部六个轨道根数的完全最小二乘解进行精确的轨道确定。由于在地球同步轨道区域存在着大量面积与质量之比大的物体，这些物体受非重力扰动的影响很大，因此实际上还必须求出描述这些影响的动力学参数，也就是说，每个轨道要有6至8个自由参数。验证是以从设在泰德天文台（加那利群岛）的欧空局空间碎片望远镜获得的一组实际数据为基础的。我们证明了，从观测的稀疏时间分布开始，可以将一组稀疏观测组合成一组具有轨道的物体，这将与能够每晚覆盖天空中感兴趣区域一次的调查相兼容。相对于以前已知的相关和轨道确定算法所需的条件，这可能会大大减少对未来望远镜网络的要求。
英文摘要：
While building up a catalog of Earth orbiting objects, if the available optical observations are sparse, not deliberate follow ups of specific objects, no orbit determination is possible without previous correlation of observations obtained at different times. This correlation step is the most computationally intensive, and becomes more and more difficult as the number of objects to be discovered increases. In this paper we tested two different algorithms (and the related prototype software) recently developed to solve the correlation problem for objects in geostationary orbit (GEO), including the accurate orbit determination by full least squares solutions with all six orbital elements. Because of the presence in the GEO region of a significant subpopulation of high area to mass objects, strongly affected by non-gravitational perturbations, it was actually necessary to solve also for dynamical parameters describing these effects, that is to fit between 6 and 8 free parameters for each orbit. The validation was based upon a set of real data, acquired from the ESA Space Debris Telescope (ESASDT) at the Teide observatory (Canary Islands). We proved that it is possible to assemble a set of sparse observations into a set of objects with orbits, starting from a sparse time distribution of observations, which would be compatible with a survey capable of covering the region of interest in the sky just once per night. This could result in a significant reduction of the requirements for a future telescope network, with respect to what would have been required with the previously known algorithm for correlation and orbit determination.
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