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Analysing the Correlation Performance of ESA's Planned Space-based GEO Surveillance Mission

机译:分析ESA规划空间地理监测特派团的相关性能

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The surveillance of the region around the geosynchronous orbit (GSO) is usually done with optical sensors, i.e. telescopes. Placing a telescope on a satellite platform and performing the observations from space has several advantages, such as independence from weather and possibly an extended coverage. Several operational sensors of this type already exist, e.g. Sapphire and SBV. The European Space Agency is also supporting the development of technologies for such a mission. The envisaged mission design would be to place the sensor on a sun-synchronous orbit close to the terminator plane to observe objects in GSO passing through dedicated fences in the anti-solar direction. In this work the correlation of tracklets is analysed, which means to test whether two tracklets originate from the same object. This is done by iterating on the hypothetical ranges and solving a boundary value problem. The results show that an initial catalogue build-up with observations only from such a sensor would be problematic because a large number of false associations is made. Even a post-processing technique based on the search for clusters in the correlations cannot reduce these false associations sufficiently. In total approx. 70% of the objects can be identified as unique clusters. Other possible influences which are not investigated in detail is the seasonal dependence of the observations and the sensitivity of the correlation to higher noise levels of the sensor. It is concluded that a sensor used with the given mission design would probably be more useful for catalogue maintenance than for catalogue build-up. However, this also has to be seen in the framework of a complete surveillance network.
机译:地质上轨道(GSO)周围的区域的监视通常用光学传感器进行,即望远镜。将望远镜放在卫星平台上并从空间进行观察有几个优点,例如自天气的独立性,可能是一个扩展的覆盖范围。已经存在了几种这种类型的操作传感器,例如,如此。蓝宝石和SBV。欧洲航天局也在支持这种使命的技术的发展。设想的使命设计将是将传感器放置在靠近终结器平面的太阳同步轨道上,以观察通过防太阳能方向的专用栅栏的GSO中的对象。在这项工作中,分析了Tracklet的相关性,这意味着测试两个Tracklet是否来自同一对象。这是通过迭代假设范围并解决边界值问题来完成的。结果表明,仅使用这种传感器的观察结果建立的初始目录将是有问题的,因为制造了大量的错误关联。甚至基于相关联的基于Clusters的后处理技术也不能充分降低这些错误关联。总共约。 70%的物体可以被识别为独特的集群。未详细研究的其他可能的影响是观察结果的季节性依赖性以及与传感器的更高噪声水平的相关性的敏感性。得出结论,与给定的任务设计一起使用的传感器对目录维护可能比目录维护更有用。然而,这也必须在完整监视网络的框架中看到。

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