The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.
A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.
The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.
MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.
This paper is supported by “Projects from the National Natural Science Foundation of China” (No. 61602041), “The Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions” (No. CIT&TCD20150314), and the “Premium Funding Project for Academic Human Resources Development in Beijing Union University” (No. BPHR2017CZ07). Furthermore, the authors would also like to thank all who have helped with this study.
Author contributions: Doctor Yanxia Liu wrote most of the paper. Professor Jianjun Fang designed the structure of the paper and helped to revise the manuscript. Doctor Gang Shi corrected part of the English expression. All authors approved the final version of the manuscript.
Conflicts of interest: The authors declare no conflict of interest.
Liu, Y., Fang, J. and Shi, G. (2019), "Calibration of magnetic compass using an improved extreme learning machine based on reverse tuning", Sensor Review, Vol. 39 No. 1, pp. 121-128. https://doi.org/10.1108/SR-04-2018-0080Download as .RIS
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