IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7228-d1133538.html
   My bibliography  Save this article

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

Author

Listed:
  • Chao Liu

    (School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
    School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China)

  • Qingjie Xu

    (School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China)

  • Ya Fan

    (Guizhou General Team, China Construction Material Industry Geology Survey Center, Guiyang 550009, China)

  • Hao Wu

    (School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China)

  • Jian Chen

    (School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China)

  • Peng Lin

    (College of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China)

Abstract

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

Suggested Citation

  • Chao Liu & Qingjie Xu & Ya Fan & Hao Wu & Jian Chen & Peng Lin, 2023. "Detection of Abnormal Data in GNSS Coordinate Series Based on an Improved Cumulative Sum," Sustainability, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7228-:d:1133538
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7228/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7228/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David McDonald, 1990. "A cusum procedure based on sequential ranks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(5), pages 627-646, October.
    2. Songliangzi Yang & Changhui Xu & Jinzhong Mi & Shouzhou Gu, 2022. "Dynamic Deformation Monitoring of Offshore Oil Platforms with Integrated GNSS and Accelerometer," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lombard, F. & Hawkins, Douglas M. & Potgieter, Cornelis J., 2017. "Sequential rank CUSUM charts for angular data," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 268-279.
    2. Yanhong Wu, 1996. "A less sensitive linear detector for the change point based on kernel smoothing method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 43(1), pages 43-55, December.
    3. Du, Lilun & Wen, Mengtao, 2023. "False discovery rate approach to dynamic change detection," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    4. Chunguang Zhou & Changliang Zou & Yujuan Zhang & Zhaojun Wang, 2009. "Nonparametric control chart based on change-point model," Statistical Papers, Springer, vol. 50(1), pages 13-28, January.
    5. Shovan Chowdhury & Amitava Mukherjee & S. Chakraborti, 2014. "On A Two-Parameter Discrete Distribution And Its Applications," Working papers 143, Indian Institute of Management Kozhikode.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7228-:d:1133538. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.