Volume 19, Issue 8 (August 2019)                   Modares Mechanical Engineering 2019, 19(8): 2031-2038 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Kianfar K, Ranjbar Noiey A, Rezaie B. Accessibility to the Geographic Location of Earth's Magnetic Field with Simulation & Data Collection. Modares Mechanical Engineering 2019; 19 (8) :2031-2038
URL: http://mme.modares.ac.ir/article-15-23598-en.html
1- Control Department, Faculty of Electrical & Computer Engineering, Babol University of Technology, Babol, Iran
2- Control Department, Faculty of Electrical & Computer Engineering, Babol University of Technology, Babol, Iran , a.ranjbar@nit.ac.ir
Abstract:   (5094 Views)

In recent years, scientific advances in navigation systems and technological development of low-power consumption and high-precision in magnetic sensors have made researchers to realize that earth’s magnetic field can be applied for locating purposes. Earth’s magnetic field is applied in the navigation method where the required data from earth’s magnetic field can be read from high accuracy magnetic sensors. It is possible to determine the location by comparing the data with the reference maps through adaption of algorithms and/or filtering. Generally, in this method of locating, the inertia system is used to determine the velocity and condition, and the magnetic navigation system represents navigational assistance. In the first step toward obtaining a magnetic locating system, a reference magnetic map must be created; so, it is required to carefully analyze the earth’s magnetic field, the quantity, and quality of the field variations over different time and places. In this paper, the possibility of obtaining the geographical location of an observatory by extracting available data of a magnetic observatory has been investigated and, then, the effect of the displacement of geographical location on the magnitude of the earth's magnetic field has been examined by an experimental test. The results of simulation and data collection confirm the fact that geographic location for a variety of vehicles can be attainable just using earth's magnetic field data and there is no need to use any other navigation sensors.

Full-Text [PDF 1125 kb]   (2151 Downloads)    
Article Type: Qualitative Research | Subject: Mechatronics
Received: 2018/07/29 | Accepted: 2019/01/30 | Published: 2019/08/12

References
1. Landry RJ, Boutin P, Constantinescu A. New anti-jamming technique for GPS and GALILEO receivers using adaptive FADP filter. Digital Signal Processing. 2006;16(3):255-274. [Link] [DOI:10.1016/j.dsp.2005.04.015]
2. Wiltschko W. The earth's magnetic field and bird orientation. Trends in Neurosciences. 1980;3(6):140-144. [Link] [DOI:10.1016/0166-2236(80)90052-1]
3. Wiltschko R, Wiltschko W. Sensing magnetic directions in birds: Radical pair processes involving cryptochrome. Biosensors (Basel). 2014;4(3):221-242. [Link] [DOI:10.3390/bios4030221]
4. Zhou J, Ge ZL, Shi GG, Liu YX. Key technique and development for geomagnetic navigation. Journal of Astronautics. 2008;5. [Link]
5. Guo CF, Hu ZD, Zhang SF, Cai H. A survey of geomagnetic navigation. Journal of Astronautics. 2009;4. [Link]
6. Mu H, Ren ZX, Hu XP, Ma HX. Information fusion strategy and performance for marine Inertial/Geomagnetic navigation system. Journal of Chinese Inertial Technology. 2007;3. [Link]
7. Xiao J, Duan X, Qi X, Shi J. A novel feature extraction method of direction navigability analysis for geomagnetic navigation. Journal of Computational Methods in Sciences and Engineering. 2018(1):47-59. [Link] [DOI:10.3233/JCM-170770]
8. Liu M, Wang H. IMM-UKF based underwater geomagnetic aided inertial navigation algorithm. Proceedings of the 2012 Second International Conference on Electric Information and Control Engineering, April 6-8, 2012. Washington DC: IEEE Computer Society; 2012. p. 793-796. [Link]
9. Guo C, Cai H, Hu Z. Nonlinear filtering techniques for geomagnetic navigation. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering. 2014;228(2):305-320. [Link] [DOI:10.1177/0954410013476639]
10. Yuan D, Ma X, Liu Y, Yang L, Wu Y, Zhang X. Research on underwater integrated navigation system based on SINS/DVL/magnetometer/depth-sensor. OCEANS 2017, 19-22 June 2017, Aberdeen, UK. Piscataway: IEEE; 2017. [Link] [DOI:10.1109/OCEANSE.2017.8084701]
11. Li H, Liu M, Liu K. Bio-inspired geomagnetic navigation method for autonomous underwater vehicle. Journal of Systems Engineering and Electronics. 2017;28(6):1203-1209. [Link]
12. Hua B, Zhang Z, Wu Y, Chen Z. Autonomous navigation algorithm based on AUKF filter about fusion of geomagnetic and sunlight directions. International Journal of Intelligent Computing and Cybernetics. 2018;11(4):471-485. [Link] [DOI:10.1108/IJICC-07-2017-0087]
13. Tkhorenko MY, Pavlov BV, Karshakov EV, Volkovitsky AK. On integration of a strapdown inertial navigation system with modern magnetic sensors. 25th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS), 28-30 May 2018, St. Petersburg, Russia. Piscataway: IEEE; 2018. [Link] [DOI:10.23919/ICINS.2018.8405845]
14. Chong Y, Chai H, Liu W, Kong Y, Pan Z, Chen G. The geomagnetic filtering algorithm based on correlative probability density add-weight. In: Sun J, Yang C, Guo S, editors. China Satellite Navigation Conference (CSNC) 2018 proceedings, CSNC 2018, lecture notes in electrical engineering. 499th Volume. Singapore: Springer; 2018. pp. 511-523. [Link] [DOI:10.1007/978-981-13-0029-5_45]
15. Chen Z, Zhang Q, Pan M, Chen D, Wan C, Wu F, et al. A new geomagnetic matching navigation method based on multidimensional vector elements of earth's magnetic field. IEEE Geoscience and Remote Sensing Letters. 2018;15(8):1289-1293. [Link] [DOI:10.1109/LGRS.2018.2836465]
16. Holschneider M, Chambodut A, Mandea M. From global to regional analysis of the magnetic field on the sphere using wavelet frames. Physics of the Earth and Planetary Interiors. 2003;135(2-3):107-124. [Link] [DOI:10.1016/S0031-9201(02)00210-8]
17. Thomson AWP, Hamilton B, Macmillan S, Reay SJ. A novel weighting method for satellite magnetic data and a new global magnetic field model. Geophysical Journal International. 2010;181(1):250-260. [Link] [DOI:10.1111/j.1365-246X.2010.04510.x]
18. Thomson AWP, Lesur V. An improved geomagnetic data selection algorithm for global geomagnetic field modelling. Geophysical Journal International. 2007;169(3):951-963. [Link] [DOI:10.1111/j.1365-246X.2007.03354.x]
19. Merrill RT, Mc Elhinny MW, Mc Fadden PL. The magnetic field of the earth: Paleomagnetism, the core, and the deep mantle. Mc Elhinny MW, Mc Fadden PL, editors. San Diego: Academic Press; 1998. [Link]
20. Pang H, Zhu XJ, Pan M, Zhang Q, Wan C, Luo S, et al. The component compensation of geomagnetic field vector measurement system. Journal of Magnetism and Magnetic Materials. 2015;381:390-395. [Link] [DOI:10.1016/j.jmmm.2015.01.031]
21. Vichare G, Rajaram R. Comparative study of models of earth's magnetic field derived from Oersted, CHAMP and SAC-C magnetic satellite data. The Journal of Indian Geophysical :union:. 2009;13(1):33-42. [Link]
22. Chulliat A, Macmillan S, Alken P, Beggan C, Nair M, Hamilton B, et al. The US/UK world magnetic model for 2015-2020 [Internet]. Washington DC: National Oceanic and Atmospheric Administration; 2015 [cited 2015 August 31]. Available from: http://nora.nerc.ac.uk/id/eprint/510709/1/WMM2015_Report.pdf [Link]
23. Backus G, Parker R, Constable C. Foundations of geomagnetism. New York: Cambridge University Press; 1996. [Link]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.