Dynamic nolinear Gauss-Helmert model and its robust total Kalman filter algorithm for GNSS-acoustic underwater positioning

被引：0
|
作者：
Kuang Y. [1 ,2 ]
Lü Z. [1 ,3 ]
Li L. [1 ]
Wang F. [1 ]
Xu G. [3 ]
机构：
[1] Institute of Surveying and Mapping, Information Engineering University, Zhengzhou
[2] Department of Combat Support, Rocket Force NCO College, Qingzhou
[3] Institute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2023年 / 52卷 / 04期
基金：
中国国家自然科学基金;
关键词：
GNSS-A technology; nonlinear GH model; robust estimation; seafloor control point; sound velocity ranging error;
D O I：
10.11947/j.AGCS.2023.20210467
中图分类号：
学科分类号：
摘要：
The GNSS-acoustic combined observing is an important means to determine the position of seafloor control points, but it will be interfered by error factors such as the uncertainty in sound velocity and the positioning deviation of the sea surface platform. However, the processing strategy of general method based on the error propagation law for various errors makes the seafloor point coordinate solution inaccurate. To solve the above problems, this paper sets the time-invariant term of sound velocity ranging as the parameter to be solved, and discusses the influence of time-varying error of sound velocity ranging and transducer position error in the coefficient matrix of underwater observation equation. Thus, the dynamic nonlinear Gauss-Helmert (GH) model for GNSS-acoustic underwater positioning is constructed, and the total Kalman filter solution of this method is derived. On this basis, taking into account the gross errors polluting of the observations, the robust method and solution steps of the new model are given. Finally, simulation experiments and a testing experiment in the sea area near Jiaozhou Bay are used to verify the performance of the new model. The results show that under conditions with no gross errors and either different water depths or different transducer position errors, the accuracy and stability of the proposed method are both higher than those of the general method. When the observations are polluted by gross errors, the robust filter algorithm of the new model can accurately identify and locate the abnormal information. The precision of its 3D point deviation results can be obviously optimized, and the solution performance is superior to that of the general method. © 2023 SinoMaps Press. All rights reserved.
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页码：559 / 570
页数：11
相关论文
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