With Global Navigation Satellite System (GNSS), landslide monitoring often uses the short baseline relative positioning mode and its monitoring sequence is mainly affected by measurement noise and multipath effects. In the real-time landslide monitoring, its results are subject to problems such as ambient environment and high cost. And multipath effect in the time series of the monitoring results will be more noticeable. In order to effectively eliminate the multipath errors in landslide monitoring results and improve the accuracy of monitoring results, this paper proposes a new idea of adaptive SSA extracting multipath errors with weighted fusion algorithm. In this regard, firstly, gross errors caused by the multipath effect will be estimated and retained considering the position. Then, extracting the multipath errors of the first three days with adaptive SSA, and analyzing its time series of the first three days to obtain the multipath error correction model, which is used for real-time correction of the time series. Through comparative analysis with traditional sidereal filtering algorithm, the results show that adaptive SSA and weighted fusion algorithm can effectively identify gross errors caused by multipath effect and obtain more accurate multipath error correction models, and improve the accuracy of real-time GNSS landslide monitoring.


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    Titel :

    Research on Adaptive SSA for GNSS Landslide Multipath Effect Elimination


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yang, Changfeng (Herausgeber:in) / Xie, Jun (Herausgeber:in) / Li, Xinrui (Autor:in) / Zhong, Chuhan (Autor:in) / Zhang, Shuangcheng (Autor:in) / Liu, Wanlin (Autor:in) / Zhou, Xin (Autor:in) / Wang, Xuqiao (Autor:in)

    Kongress:

    China Satellite Navigation Conference ; 2022 ; Beijing, China May 22, 2022 - May 25, 2022



    Erscheinungsdatum :

    2022-05-07


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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