基于拉伊达准则的GNSS变形监测异常数据识别算法

Abnormal Data Recognition Algorithm for GNSS Deformation Monitoring Based on Laida Criterion

对采集的变形体的监测数据进行检验,准确地识别数据中的变形信息,这对于变形体的形变监测具有重要的意义。针对累积和控制图(Cumulative Sum, CUSUM)对大偏移变形数据检验时误报率较高的问题,笔者将拉伊达准则应用到检验变形监测数据的异常值中来。通过实验充分验证了拉伊达准则的检验能力,二者可以互为补充,更好地检验异常数据。实验结果表明,在检验数据的小偏移变形方面, CUSUM的检验效果较好。拉伊达准则适用于对3倍标准差以上的连续大偏移变形数据的检验,且可以较为真实地反映出检测数据的变化趋势,进而能够有效地分析监测数据的变形信息。

It is of great significance to check the monitoring data collected and accurately identify the deformation information in the data. In view of the high false alarm rate of Cumulative Sum (CUSUM) in testing large migration deformation data, the Laida criterion is applied to check the abnormal value of deformation monitoring data. The test ability of the Laida criterion is fully verified by experiments. The two criteria can complement each other and test abnormal data better. The experimental results show that CUSUM is effective in detecting small offset deformation of data. The Laida criterion is applicable to the test of continuous large migration deformation data with standard deviation of more than 3 times, and it can reflect the trend of the measured data more truthfully, and effectively analyze the deformation information of the monitoring data.