Solution for the Robust Estimation of Heterogeneous Data Fusion Based on Classification Estimation
Publication: Journal of Surveying Engineering
Volume 150, Issue 3
Abstract
Data fusion is an important issue as multi-source observation technology is widely used in geoscience. Although the problem of robust estimation exists widely in the data fusion, few solutions have been reported. This paper investigates a solution for the robust estimation of heterogeneous data fusion implementing classification, robust estimation, and data fusion. A new approach based on estimation is constructed to define accurate scales for multi-source observation data and, adopting the Institute of Geodesy and Geophysics (IGG)III weight function, an iterative algorithm is proposed for the above problem. Finally, two instances are considered in verifying the feasibility of the presented solution.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author upon request: the simulation results, code of simulation, and estimation computations for MATLAB R2016a.
Acknowledgments
The authors are grateful to the editors and two reviewers for their careful work and helpful comments, which led to a significant improvement in this paper. This work is sponsored by the Natural Science Foundation of China (Grant No. 41601501).
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Published In
Journal of Surveying Engineering
Volume 150 • Issue 3 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 5, 2023
Accepted: Jan 23, 2024
Published online: Mar 26, 2024
Published in print: Aug 1, 2024
Discussion open until: Aug 26, 2024
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