Detection of Abnormal Data in GNSS Coordinate Series Based on an Improved Cumulative Sum

The global navigation satellite system (GNSS), as a high-time resolution and high-precision measurement technology, has been widely used in the field of deformation monitoring. Owing to influence uncontrollable factors, there are inevitably some abnormal data GNSS monitoring series. Thus, it is necessary detect identify series improve accuracy reliability disaster law analysis warning. Many methods can be data, among which statistical process control theory, represented by cumulative sum (CUSUM), used. CUSUM usually constructs statistics determines limits based on threshold criteria average run length (ARL) then uses statistics. However, different degrees ‘trailing’ phenomenon exist interval identified algorithm, leading higher false alarm rate. Therefore, we propose an improved method that breaks for additive season trend (BFAST) instead ARL-based identification. coordinate results show compared with CUSUM, shows stronger robustness, more accurate detection significantly lower