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Acta Geodaetica et Geophysica

, Volume 53, Issue 2, pp 295–307 | Cite as

Solution for GNSS height anomaly fitting of mining area based on robust TLS

  • Yeqing Tao
  • Guangxiong Mao
  • Xiaozhong Zhou
Original Study

Abstract

Global navigation satellite system (GNSS) height solutions of mining area are readily contaminated by outliers because of the special geological environment. Additionally, GNSS height anomaly fitting model is a type of errors-in-variables model, and the traditional solution for parameter estimation does not account for error in the coefficient matrix. To solve these two problems, this paper presents a solution of the robust total least squares estimation for GNSS height anomaly fitting of mining area. Different from the traditional solution for robust estimation, an algorithm is established employing median method to obtain stable parameter values under the condition that observation data are highly contaminated. Employing Lagrange function and weight function, an iterative algorithm for the parameter estimation of GNSS anomaly fitting model is proposed, and the algorithm is verified using real data of mining area. The numerical results show that the proposed solution obtains stable parameter values when observation data are highly contaminated by outliers and demonstrate that the proposed algorithm is more accurate than traditional solutions for robust estimation.

Keywords

Errors-in-variables model Total least squares Robust estimation Median method Height anomaly of mining area 

Notes

Acknowledgements

The authors would like to thank the reviewers and the editor. This research was supported by the National Natural Science Foundation of China (41601501; 41271135), Natural Science Found for Colleges and Universities of Jiangsu Province (16KJD420001) and Huaian Key Laboratory of Geographic Information Technology and Applications (HAP201405).

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Copyright information

© Akadémiai Kiadó 2018

Authors and Affiliations

  1. 1.School of Urban and Environmental SciencesHuaiyin Normal UniversityHuaianPeople’s Republic of China

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