Acta Geodaetica et Cartographica Sinica ›› 2020, Vol. 49 ›› Issue (8): 955-964.doi: 10.11947/j.AGCS.2020.20190417

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Conditional variance stationarity evaluation method for GNSS ambiguity decorrelation

LU Liguo1,2, LIU Wanke3, LU Tieding2, MA Liye4, WU Tangting2, YANG Yuanxi1   

  1. 1. State Key Laboratory of Geo-Information Engineering, Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China;
    2. Faculty of Geomatics, East China University of Technology, Nanchang 330013, China;
    3. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
    4. GNSS Research Center, Wuhan University, Wuhan 430079, China
  • Received:2019-10-12 Revised:2020-05-26 Published:2020-08-25
  • Supported by:
    The National Natural Science Foundation of China (Nos. 41804020;41774031);The National Key Research and Development Program (No. 2016YFB0501405);The Natural Science Foundation of Jiangxi Province of China (Nos. 20202BAB212010;20192BAB217011)

Abstract: GNSS ambiguity decorrelation is to optimize the permutation order of conditional variance by integer transformation, so as to improve the search efficiency. One of the key problems is how to evaluate the relationship between decorrelation and conditional variance. Aiming at this problem, this paper theoretically analyzes the numerical relationship between decorrelation and conditional variance after sorting. It is found that the decorrelation performance is related to the stationarity of the conditional variance sequence. The stronger the decorrelation performance, the more stable the conditional variance sequence. So based on this theoretical basis, the conditional variance stationarity is proposed as an index to evaluate the performance of decorrelation. The results are verified by both simulation and actual test experiments, and the conditional variance trend graph as well as search time are also used to qualitatively and quantitatively evaluate the performance of decorrelation, to determine the rationality of the conditional variance stationarity. The experimental results show that the conditional variance stationarity proposed in this paper can more accurately and intuitively measure the performance of ambiguity decorrelation. The index defined in this paper reveal the essence of GNSS ambiguity decorrelation.

Key words: GNSS, ambiguity, decorrelation, conditional variance, evaluation index

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