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Analysis and Simulation of Geomagnetic Map Suitability Based on Vague Set

Published online by Cambridge University Press:  18 April 2016

Lihui Wang*
Affiliation:
(Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China)
Le Yu
Affiliation:
(Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China)
Nan Qiao
Affiliation:
(Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China)
Desheng Sun
Affiliation:
(Beijing Institute of Aerospace Control Devices, Beijing 100039, China)
*

Abstract

An evaluation method named vague set is proposed to describe the suitability of a geomagnetic map. It is based on the Fuzzy Decision Making (FDM) method, and overcomes the FDM model's shortcomings that favouring and opposing content cannot be taken into account simultaneously. The membership function and non-membership function are used to define the influence of the geomagnetic map parameters on map suitability, including standard deviation, information entropy, roughness and slope variance. The weight of each geomagnetic map parameter is calculated by establishing an optimisation model. Vague set data are divided into four types after classification, and Weighted Score Function Values (WSFVs) of matching areas are obtained by using the Weighted Score Function (WSF) method. Then, WSFV of each matching area are compared to select an optimal area. Simulation results demonstrate that geomagnetic map suitability is positively proportional to the function value, and matching error is negatively proportional to the WSFV of the matching area.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Behbood, V., Lu, J., Zhang, G. and Pedrycz, W. (2015). Multistep fuzzy bridged refinement domain adaptation algorithm and its application to bank failure prediction. Fuzzy Systems, IEEE Transactions on, 23(6), 19171935.Google Scholar
Fan, Z.P., You, T.H. and Zhang, Q. (2002). The Sensitivity Analysis to Attribute Values Based on the Additive Weighting Model in Multiple Attribute Decision Making. Journal of Northeastern University, 23(1), 8386.Google Scholar
Galvan-Tejada, C.E., Garcia-Vazquez, J.P., Galvan-Tejada, J.I. and Brena, R. (2015). Multivariate or univariate model analysis for indoor location systems: A comparison. Electronics, Communications and Computers (CONIELECOMP), Cholula: IEEE, 116120.Google Scholar
Gau, W.L. and Buehere, D.J. (1993). Vague sets. IEEE Transactions on Systems, Man and Cybernetics, 1993, 23(2), 610614.Google Scholar
Hu, X.P., and Wu, M.P. (2013). Technologies on Underwater Geomagnetic Field Navigation. Beijing:National Defense Industry Press, pp. 9498.Google Scholar
Min-Ho, Ka., Baskakov, A.I., Terechov, V.A. and Kononov, A.A. (2013). Estimation of the Sea-Surface Slope Variance Based on the Power Spectrum Width of a Radar Scatterometer. Geoscience and Remote Sensing Letters, IEEE, 10(3), 593597.Google Scholar
Odejobi, O.A., Wong, S.H.S. and Beaumont, A.J.A. (2007). fuzzy decision tree-based duration model for standard Yoruba text-to-speech synthesis. Computer Speech and Language, 21(2), 325349.CrossRefGoogle Scholar
Ouazraoui, N., Bourareche, M. and Nait-Said, R. (2015). Fuzzy modelling of uncertain data in the layers of protection analysis. Industrial Engineering and Operations Management (IEOM), IEEE, Dubai, pp. 16.Google Scholar
Wang, H.X. (2010). Converted equations from Fuzzy valued data to Vague valued data. Computer Engineering and Applications, 46(25), 4748.Google Scholar
Wang, H.X., Zhang, F.J. and Zheng, Z.L. (2012). A Vague Decision Method and its Application in Choosing Tailings Dam Locations. Industrial Control and Electronics Engineering (ICICEE), IEEE, Xi'an, 12611264.Google Scholar
Wang, L.H. and Yu, L. (2015). Construction Method of the Topographical Features Model for Underwater Terrain Navigation. Polish Maritime Research, SI(22), 121125.Google Scholar
Wang, S.C., Wang, Z., Zhang, J.S. and Qiao, Y.K. (2009). Technology of preparation of reference map using total geomagnetic intensity gradient module. Systems Engineering and Electronics, 31(4), 881885.Google Scholar
Wang, X.L. (2011). The study of some key technologies in geomagnetic matching navigation. Engineering of Surveying and Mapping, 20(1), 15.Google Scholar
Wu, D.S., Zhang, G.Q. and Lu, J. (2015). A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services. IEEE Journals and Magazines, 23(1), 2943.Google Scholar
Xu, C.L. and Wei, L.L. (2010). Vague set method of multi-criteria fuzzy decision making. Systems Engineering-Theory & Practice. 30(11), 20192025.Google Scholar
Zhang, T., Xu, X.S., Li, P.J. and Wang, Q. (2009). Selection criteria for matching area in terrain aided navigation based on fuzzy decision. Journal of Dalian Maritime University, 35(1), 58.Google Scholar