GNSS坐标序列的自适应奇异谱分析

Adaptive singular spectrum analysis of GNSS coordinate sequence

针对奇异谱分析在进行大量GNSS高程时间序列时变周期信号提取时存在繁琐而低效的问题,提出一种自适应奇异谱分析方法。通过改进迹矩阵的构建方式提高算法运行效率,并结合频谱分析辅助确定滞后窗口大小,同时采用迭代法自主选择主分量,从而精准高效地提取时间序列中的周期项信息。实例分析表明,该方法能自适应地设置滞后窗口大小,准确提取大量GNSS高程时间序列中的时变周期信号,且单个时间序列提取效率是奇异谱分析的6倍,该方法的自适应性和准确性使其更加适用于大量GNSS站点的时变周期信号分析。

In view of the cumbersome and inefficient problem of singular spectrum analysis in extracting time-varying periodic signals from a large number of GNSS elevation time series,an adaptive singular spectrum analysis58E method is proposed in this paper.The track matrix was optimized to improve the efficiency of the algorithm.The frequency spectrum analysis was used to assist in determining the size of the lag window and the iterative method independently selected the principal component so as to extract the periodic term information accurately and efficiently.Based on comparative experiments,the running time and correlation analysis results indicate that the proposed method can adaptively set the embedding window size,accurately extract the time-varying periodic signals in a large number of GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.Also,the method is intelligent and accurate and is more suitable for time-varying periodic signal analysis of global GNSS sites.