Extracting White Noise Statistics in GPS Coordinate Time Series
Extracting White Noise Statistics in GPS Coordinate Time Series
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DOI：
10.1109/lgrs.2012.2213576
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发表时间：
2012-04
影响因子：
4.8
通讯作者：
J. Montillet;P. Tregoning;S. McClusky;Kegen Yu
中科院分区：
文献类型：
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作者：
J. Montillet;P. Tregoning;S. McClusky;Kegen Yu
GPS坐标时间序列中的噪声服从不同分量(白噪声、闪烁噪声和随机游动)的幂函数噪声模型。本文提出了一种利用经验模式分解算法将GPS坐标时间序列分解为一系列子时间序列来估计白噪声统计量的算法。该算法首先估计每个子时间序列的Hurst参数，然后根据Hurst参数准则选择与白噪声相关的子时间序列。利用模拟的GPS坐标时间序列和实际数据对该方法进行了检验，并与标准(CATS软件)最大似然(ML)估计法的结果进行了比较。结果表明，该算法具有很低的计算复杂度，在白噪声幅度的不确定度适度增加(~5%)的情况下，比CATS最大似然算法快100倍以上。可靠的白噪声统计在一系列应用中很有用，包括改进GPS时间序列的过滤，检查估计的同震偏移的有效性，以及估计场地速度的无偏不确定性。该算法复杂度低，计算效率高，大大提高了大地测量时间序列的处理速度。
The noise in GPS coordinate time series is known to follow a power-law noise model with different components (white noise, flicker noise, and random walk). This work proposes an algorithm to estimate the white noise statistics, through the decomposition of the GPS coordinate time series into a sequence of sub time series using the empirical mode decomposition algorithm. The proposed algorithm estimates the Hurst parameter for each sub time series and then selects the sub time series related to the white noise based on the Hurst parameter criterion. Both simulated GPS coordinate time series and real data are employed to test this new method; the results are compared to those of the standard (CATS software) maximum-likelihood (ML) estimator approach. The results demonstrate that this proposed algorithm has very low computational complexity and can be more than 100 times faster than the CATS ML method, at the cost of a moderate increase of the uncertainty (~5%) of the white noise amplitude. Reliable white noise statistics are useful for a range of applications including improving the filtering of GPS time series, checking the validity of estimated coseismic offsets, and estimating unbiased uncertainties of site velocities. The low complexity and computational efficiency of the algorithm can greatly speed up the processing of geodetic time series.