基于单频GPS测量的LEO卫星实时导航算法的实现与调优

A. Avanzi, A. García, P. Tortora
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引用次数: 3

摘要

GNSS系统自诞生以来,提供的导航服务已被应用到各种领域,其中一些领域超出了基础设施最初的范围。利用星载GNSS接收机实现低轨道卫星自主定轨就是其中一种。可实现的定位精度约为10 m 3d rms,满足大部分任务要求,并且可以使用基于松散观测约束的简单导航算法实现。对于那些需要更好的实时精度的任务,可以通过减少动态滤波来实现亚米定位,由于详细的轨道力模型,它提供了很强的观测约束。该算法的通用架构在文献中得到了很好的介绍,尽管其在现实世界中的实现仍然局限于少数情况,部分原因是很少有任务需要实时如此精确,部分原因是其实现的复杂性。本文讨论了这种复杂性的一个方面,它与过滤器调优有关,或者换句话说,与观察和过程误差的统计描述的有效定义有关。由于滤波器调谐在大多数情况下是一个反复试验的过程,同时也是一个微妙的实现步骤,可能导致不稳定和发散,因此本文提出了一种从简化的动态算法实现中实现稳定亚米定位的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation and tuning of LEO satellites real-time navigation algorithm based on single frequency GPS measurements
Since its origin, the navigation services provided by the GNSS system have been applied to a variety of field, some of which are beyond the original scope of the infrastructure. LEO satellites autonomous orbit determination by means of on board GNSS receiver is one of the cases. The achievable position accuracy, in the order of 10 m 3d rms, satisfies most of the mission requirements and can be implemented with a simple navigation algorithm based on loose constraining of observations. For those missions that require better real time accuracies, sub meter positioning is achievable, by means of reduced dynamic filtering, which provides strong constraining of observations thanks to a detailed orbit force model. The algorithm general architecture is well covered in literature, although its real world implementation is still limited to few cases, partially because few are the missions that require such accuracy in real time, and partially because of its implementation complexity. This paper addresses one aspect of this complexity, which is related to filter tuning, or in other words to the effective definition of a statistical description of observations and process errors. Being the filter tuning a process of trial and error in most cases, and a delicate implementation step at the same time which may lead to instability and divergence, this paper proposes a procedure to achieve stable sub meter positioning from the reduced dynamic algorithm implementation.
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