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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章利用GPS和北斗星座实现含糊度解算的高可靠性的卫星选择算法

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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度分辨率(AR)的可靠性取决于模糊度成功率(ASR),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该模糊度。使用当前的GPS星座图,只有在一定百分比的时间才能实现基于几何的模型的足够高的ASR。结果,不能通过单个星座来确保AR的高可靠性。如果采用双星座系统(DCS),例如GPS和北斗,它们可以提供更多的卫星,那么用户可以期待显着的性能优势,例如AR可靠性和高精度定位解决方案。简单地将所有卫星用于AR和定位是一个简单的解决方案,但并不一定会带来希望的高可靠性。本文提出了一种替代方法,该方法选择可见卫星的子集以在多GNSS环境中实现AR解决方案的更高可靠性,而不是使用所有卫星。传统上,卫星选择算法主要基于精度的位置稀释(PDOP),以满足精度要求。在此贡献中,引入了一些用于GNSS卫星选择的可靠性标准,并开发了一种用于可靠歧义分辨率(SARA)的新颖的卫星选择算法。 SARA算法允许接收机选择卫星子集以实现较高的ASR(例如0.99以上)。模拟双星座案例的数值结果表明,使用SARA程序,ASR值超过0.99的百分比和通过阈值3的比率测试值的百分比都高于直接使用所有卫星的角度,特别是在在双星座的情况下,ASR的百分比(> 0.99)和比率测试值(> 3)分别高达98.0%和98.5%,而没有进行卫星选择的情况则分别为18.1%和25.0%。还值得注意的是,SARA的实现简单且计算时间短,可以在大多数实时数据处理应用中使用。

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