基于最小噪声系数的伽利略多频观测组合方法

Rong Yuan, Shengli Xie, Feng Gao, Zhenni Li
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引用次数: 0

摘要

经典多频组合主要是建立一个长波长组合,消除电离层延迟组合,组合噪声系数大于1。在无差分和无组合观测数据的基础上,提出了一种基于最小噪声系数原则的通用实系数组合方法。组合噪声系数小于1,可适用于任意多频观测组合。根据伽利略各信号的频率,给出了伽利略多频噪声系数的最优组合以及理论上提高精度的效果。与单频观测相比,双频、三频、四频和五频的最佳组合观测精度分别提高了40%、49%、54%和59%。通过对实际伽利略三频E1、E5A和E5b信号的观测验证,双频和三频的最优组合比单频观测精度分别提高52%和62%。根据实际测量,最小噪声系数组合的观测精度提高与理论分析基本一致。最后,分析了最小噪声组合与无差分非组合观测值之间的等价性,结果表明,最小噪声组合是无差分非组合观测值的最优权重模型,其优化准则为观测噪声最小。
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Galileo Multi-frequency Observation Combination Method Based on Minimum Noise Coefficients
Classic multi-frequency combination is mainly to build a long wavelength combination and eliminate the ionospheric delay combination, and the combined noise factor is greater than 1. On the basis of un-differenced and un-combining observations, this paper proposes a general real coefficient combination based on the principle of minimum noise coefficients. The combined noise coefficient is less than 1, which can be applied to any multi-frequency observation combination. According to the frequency of each signal frequency of Galileo, we give the optimal combination of Galileo multi-frequency noise coefficients and the effect of theoretical improving accuracy. Compared with single frequency observation, the optimal combination of dual frequency triple frequency, four frequency and five frequency improve the observation accuracy by 40%, 49%,54% and 59% respectively. It is verified by the observation of the actual Galileo three frequency E1, E5A and E5b signals, the optimal combination of dual frequency and triple frequency improves the observation accuracy by 52% and 62% respectively compared with single frequency observation. According to the actual measurements, the observation accuracy improvement of minimum noise coefficients combination is basically consistent with the theoretical analysis. Finally, we analyze the equivalence between the minimum noise combination and the un-differenced un-combining observations, the results show that the minimum noise combination is an optimal weight model of un-differenced un-combining observations, which the optimization criterion is the minimum observation noise.
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