GNSS坐标时间序列周期项自动提取方法

Automatic Extraction Method of Period Term in GNSS Coordinate Time Series

针对目前对坐标时间序列周期项探测的方式受噪声和旁瓣的影响较大,且不利于大规模数据处理的问题,提出了一种基于显著性水平的周期项自动探测识别方法,并利用仿真数据验证了其性能。然后利用该方法对全球分布的1 082个测站的坐标时间序列进行了周期项提取。研究结果表明,该方法能够在噪声和旁瓣的影响下实现对潜在周期项的自动探测和提取,并且具有良好的性能。在仿真实验中,当信噪比为-10 dB时仍具有70%的准确率。而通过对N、E、U这3个方向的实测数据的周期项进行提取,发现在全球范围内具有共性的周期项除了周年项和半周年项,还有时间跨度为120、88、71、59、50和14 d左右的周期项。这与相关学者的研究结果一致,证明了该方法的实用价值。

The existing method for periodic detection and identification in coordinate time series is easily affected by noise and sidelobes, and is not conducive to large-scale data processing. In order to solve this problem, a method based on significance level is proposed. The method could automatically detect and identify the periods in GNSS coordinate time series. After the performance is verified by simulation data, this method is used to extract the periods in the coordinate time series of 1 082 stations distributed globally. The result shows that the method can realize the automatic detection and extraction of potential periods under the influence of noise and sidelobes, and has good performance. In the simulation experiment, it still has an accuracy of 70% when the SNR is-10 dB. And by extracting the periods of the measured data in the three directions of N, E and U, it is found that in addition to the annual and semi-annual period, there are periods with spans of about 120, 88, 71, 59, 50 and 14 d around the world. It is consistent with the research results of related scholars, which proves the feasibility of this method in practical application.