GPS network noise analysis: a case study of data collected over an 18-month period
GPS network noise analysis: a case study of data collected over an 18-month period
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DOI：
10.1080/14498596.2016.1138900
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发表时间：
2016-04
影响因子：
1.9
通讯作者：
S. Nistor;A. Buda
中科院分区：
文献类型：
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作者：
S. Nistor;A. Buda
对于GPS速度分量的误差分析，需要考虑很多因素，包括随机模型的确定。在本文中，我们打算分析通过使用18个月期间收集的数据获得的结果。对于GPS站坐标时间序列中包含的噪声，建议采用对坐标时间序列的线性趋势进行拟合，然后对残差的噪声特性进行建模。对于噪声的确定，我们使用一阶高斯-马尔可夫模型- FOGM。为了观察所选噪声模型的行为，我们使用了残差的功率谱密度和对数似然值，并将结果与幂律加白噪声模型的结果进行了比较。在估计过程中，我们使用半年一次的正弦信号来表示季节影响。计算了季节变化的幅值和相位滞后。虽然在相对较短的数据跨度（短于2.5年）中有较大贡献的噪声被认为是白噪声，但使用平均值我们已经表明，必须考虑到有色噪声的存在。
For the error analysis of the GPS velocity component we have to consider many factors, including the determination of the stochastic model. In this article we intended to analyse the results obtained by using data gathered during a period of 18 months. The presence of the noise contained within the GPS station coordinate time series is recommended to be fitted by using a linear trend to the coordinate’s time series and after this procedure to model the noise properties of the residuals. For the noise determination we used a first-order Gauss-Markov model – FOGM. To see the behaviour of the chosen noise model, we used the power spectra density of the residuals and the log likelihood values and we compared the results with those from the power law plus white noise model. In the estimation process we used the semiannual sinusoidal signal to express the seasonal influence. The amplitudes and phase lag of the seasonal variation were also computed. Although the noise that has a large contribution in a relatively short data span – shorter than 2.5 years – is considered to be white noise, using the average we have shown that the presence of coloured noise has to be taken into account.