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Chapter 1 A Satellite Selection Algorithm for Achieving High Reliability of Ambiguity Resolution with GPS and Beidou Constellations

机译:第1章卫星选择算法,实现了GPS和北斗星座的歧义分辨率高可靠性

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Reliability of carrier phase ambiguity resolution (AR) of an integer least-squares (ILS) problem depends on ambiguity success rate (ASR), which in practice can be well approximated by the success probability of integer bootstrapping solutions. With the current GPS constellation, sufficiently high ASR of geometry-based model can only be achievable at certain percentage of time. As a result, high reliability of AR cannot be assured by the single constellation. In the event of dual constellations system (DCS), for example, GPS and Beidou, which provide more satellites in view, users can expect significant performance benefits such as AR reliability and high precision positioning solutions. Simply using all the satellites in view for AR and positioning is a straightforward solution, but does not necessarily lead to high reliability as it is hoped. The paper presents an alternative approach that selects a subset of the visible satellites to achieve a higher reliability performance of the AR solutions in a multi-GNSS environment, instead of using all the satellites. Traditionally, satellite selection algorithms are mostly based on the position dilution of precision (PDOP) in order to meet accuracy requirements. In this contribution, some reliability criteria are introduced for GNSS satellite selection, and a novel satellite selection algorithm for reliable ambiguity resolution (SARA) is developed. The SARA algorithm allows receivers to select a subset of satellites for achieving high ASR such as above 0.99. Numerical results from a simulated dual constellation cases show that with the SARA procedure, the percentages of ASR values in excess of 0.99 and the percentages of ratio-test values passing the threshold 3 are both higher than those directly using all satellites in view, particularly in the case of dual-constellation, the percentages of ASRs (>0.99) and ratio-test values (>3) could be as high as 98.0 and 98.5 % respectively, compared to 18.1 and 25.0 % without satellite selection process. It is also worth noting that the implementation of SARA is simple and the computation time is low, which can be applied in most real-time data processing applications.
机译:整数最小二乘(ILS)问题的载波相位模糊分辨率(AR)的可靠性取决于模糊的成功率(ASR),其在实践中可以通过整数自举解决方案的成功概率很好地近似。利用当前的GPS星座,只能在一定比例的时间内实现基于几何模型的足够高的ASR。结果,单个星座不能保证AR的高可靠性。在Dual Constellations系统(DCS)的情况下,例如,GPS和Beidou提供了更多卫星的视图,用户可以预期具有显着性能的优势,例如AR可靠性和高精度定位解决方案。只需使用所有卫星的视图,对于AR和定位是一种直接的解决方案,但并不一定导致其希望的高可靠性。本文介绍了一种替代方法,可选择可见卫星的子集,以实现多GNSS环境中的AR解决方案的更高可靠性性能,而不是使用所有卫星。传统上,卫星选择算法主要基于精度(PDOP)的位置稀释,以满足精度要求。在这一贡献中,为GNSS卫星选择引入了一些可靠性标准,并且开发了一种用于可靠性模糊分辨率(SARA)的新型卫星选择算法。 SARA算法允许接收器选择用于实现高ASR的卫星子集,例如高于0.99。来自模拟双星座壳体的数值结果表明,利用SARA程序,超过0.99的ASR值的百分比和通过阈值3的比率 - 试验值的百分比高于直接使用所有卫星的视图,特别是在双星座的情况,ASRS(> 0.99)和比率 - 试验值(> 3)的百分比分别可以高达98.0和98.5%,而没有卫星选择过程的18.1和25.0%。值得注意的是,SARA的实现简单,计算时间很低,可以在大多数实时数据处理应用程序中应用。

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