Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-26T23:54:48.988Z Has data issue: false hasContentIssue false
  • English
  • Français

An Adaptive △M-ICCP Geomagnetic Matching Algorithm

Published online by Cambridge University Press:  12 December 2017

Jing Xiao ,
Xiusheng Duan  and
Xiaohui Qi
Jing Xiao
Affiliation:
(Army Engineering University, No. 97, Hepingxi Road, Shijiazhuang 050003, PR China)
Xiusheng Duan*
Affiliation:
(Army Engineering University, No. 97, Hepingxi Road, Shijiazhuang 050003, PR China) (Shijiazhuang Tiedao University, No. 17, North Second Ring Road, Shijiazhuang 050043, PR China)
Xiaohui Qi
Affiliation:
(Army Engineering University, No. 97, Hepingxi Road, Shijiazhuang 050003, PR China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, a novel method is proposed to generate the matching sequence of an ICCP algorithm for aircraft geomagnetic-aided navigation based on the M coding principle. The length of the matching sequence and the selection of the matching points directly affects the performance of the Iterated Closest Contour Point (ICCP) algorithm. This study proposes an adaptive geomagnetic matching method, ΔM-ICCP, to solve the problem of selecting suitable matching lengths, and matching points, when a vehicle is moving in a highly dynamic environment. First, the △M coding principle is adopted to select the matching points based on the information of the magnetic field, the resolution of the magnetic map, and the accuracy of the magnetic sensor. Then, the problem of selecting parameters for the △M-ICCP algorithm is turned into an optimisation problem, which can be solved by a Binary Particle Swarm Optimisation (BPSO) algorithm. Finally, the algorithm is verified through simulation experiments. The proposed algorithm can provide a basis to determine the matching length of the △M-ICCP algorithm and adaptively adjust the algorithm's parameters according to different trajectories. The algorithm is applicable even in the areas where the fluctuations of Earth's magnetic field are not significant.

Keywords

Geomagnetic-aided navigationICCP algorithm△M codingBPSO algorithm
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 649 - 663
Copyright
Copyright © The Royal Institute of Navigation 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, C.J. (2000). Navigation system for an autonomous underwater vehicle. Master Thesis of Royal Institute of Technology, Sweden.Google Scholar
Anonsen, K.B. and Hallingstad, O. (2006). Terrain Aided Underwater Navigation Using Point Mass and Particle Filters. IEEE Position, Location, & Navigation Symposium, California, USA.Google Scholar
Baird, C.A., Snyder, F.B.and Beierle, M. (1990). Terrain-aided altitude computations on the AFTI/F-16. IEEE Position Location and Navigation Symposium, Nevada, USA.Google Scholar
Bergström, P. and Edlund, O. (2016). Robust registration of surfaces using a refined iterative closest point algorithm with a trust region approach. Numerical Algorithms, 74(3), 1–25.Google Scholar
Besl, P.J. and Mckay, H.D. (2002). A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis & Machine Intelligence, 14(2), 239–256.Google Scholar
Gounley, R., White, R.and Gai, E. (2012). Autonomous satellite navigation by stellar refraction. Journal of Guidance Control & Dynamics, 7(2), 129–134.Google Scholar
Hinrichs, P.R. (1976). Advanced terrain correlation techniques. IEEE Position, Location and Navigation Symposium, Texas, USA.Google Scholar
Huang, B., Sun, Y., Wang, L., Liu, J.and Gao, X. (2012). Iterative evaluation matching algorithm for geomagnetic navigation. Journal of Nanjing University of Aeronautics & Astronautics, 44(4), 565–569. (in Chinese)Google Scholar
Jiménez, F., Monzón, S.and Naranjo, J.E. (2016). Definition of an enhanced map-matching algorithm for urban environments with poor GNSS signal quality. Sensors, 16(2), 193.CrossRefGoogle ScholarPubMed
Komiya, K., Miyashita, S.and Maruoka, Y. (2015). Control of Autonomous Mobile Robot Using Map Matching with Optimized Search Range. Electrical Engineering in Japan, 190(4), 66–75.CrossRefGoogle Scholar
Liang, Y. (2010) Research on technologies of INS/ Geomagnetic Matching Integrated Navigation System. Ph.D Thesis of Harbin Engineering University. (in Chinese)Google Scholar
Liu, M., Wang, B.and Deng, Z. (2014). Improved ICCP algorithm and its application in gravity matching aided inertial navigation system. Chinese Control Conference, Nanjing, China.CrossRefGoogle Scholar
Maus, S. (2010). An ellipsoidal harmonic representation of earth's lithospheric magnetic field to degree and order 720. Geochemistry Geophysics Geosystems, 11, Q06015.CrossRefGoogle Scholar
Mora, H., Mora-Pascual, J.M., García-García, A.and Martínez-González, P. (2016). Computational analysis of distance operators for the iterative closest point algorithm. Plos One, 11(10), e0164694.CrossRefGoogle ScholarPubMed
Nemec, D., Janota, A., Hruboš, M.and Šimák, V. (2017). Intelligent real-time MEMS sensor fusion and calibration. IEEE Sensors Journal, 16(19), 7150–7160.Google Scholar
Nyatega, C.O. and Li, S.X. (2015). Study on geomagnetic-matching technology based on ICP algorithm. International Journal of Science & Research, 4(4), 3258–3261.Google Scholar
Nygren, I. and Jansson, M. (2004). Terrain Navigation for Underwater Vehicles Using the Correlator Method. IEEE Journal of Oceanic Engineering, 29(3), 906–915.Google Scholar
Papoyan, A., Shmavonyan, S., Khanbekyan, A., Khanbekyan, K., Marinelli, C.and Mariotti, E. (2016). Magnetic-field-compensation optical vector magnetometer. Applied Optics, 55(4), 892–895.Google Scholar
Schönau, T., Zakosarenko, V., Schmelz, M., Stolz, R.and Anders, S. (2017). Absolute calibration of a three-axis squid-cascade vector magnetometer. Measurement Science & Technology, 28(1), 015107.CrossRefGoogle Scholar
Shahbazi, M., Sohn, G., Théau, J.and Ménard, P. (2015). Robust sparse matching and motion estimation using genetic algorithms. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-3/W2(3), 197–204.Google Scholar
Shockley, J.A. and Raquet, J.F. (2015). Navigation of ground vehicles using magnetic field variations. Navigation, 61(4), 237–252.Google Scholar
Song, Z.G. (2015). The improved ICCP algorithm based on Procrustes analysis for geomagnetic matching navigation. International Conference on Design, Manufacturing and Mechatronics, Wuhan, China.Google Scholar
Teixeira, F.C. (2007). Terrain-Aided Navigation and Geophysical Navigation of Autonomous Underwater Vehicles. Ph.D Thesis of Instituto Superior Técnico.Google Scholar
Teixeira, F.C. (2012). AUV Terrain-Aided Doppler Navigation using Complementary Filtering. Manoeuvring and Control of Marine Craft, 45, 313–318.Google Scholar
Teixeira, F.C. and Pascoal, A.M. (2008). Geophysical navigation of autonomous underwater vehicles using geomagnetic information. The 2nd IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles, Killaloe, Ireland.Google Scholar
Teixeira, F.C., Quintas, J.and Pascoal, A. (2015). AUV Terrain-Aided Navigation using a Doppler Velocity Logger. IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles, 48, 137–142.Google Scholar
Wang, L., Yu, L., Qiao, N.and Sun, D. (2016). Analysis and simulation of geomagnetic map suitability based on vague set. Journal of Navigation, 69(5), 1114–1124.CrossRefGoogle Scholar
Wang, S., Lu, Z.and Zhang, J. (2015). Evaluation of ICCP algorithm for geomagnetic navigation based on hardware-in-the-loop simulation. Journal of Beijing University of Aeronautics & Astronautics, 41(2), 187–192.Google Scholar
Wang, Y.G., Jia, L., Shan, B.and Yan, T. (2014). Adaptive SA-ACO geomagnetic matching navigation algorithm. Journal of Chinese Inertial Technology, 22(1), 89–93.Google Scholar
Xiao, J., Duan, X.S., Qi, X.Hand Wang, J.C. (2017). Direction navigability analysis for geomagnetic navigation based on parallel convolutional neural networks. Journal of Chinese Inertial Technology, 25(3), 146–152. (in Chinese)Google Scholar
Zhou, J., Liu, Y.and Ge, Z. (2010). Geomagnetic matching algorithm based on probabilistic neural network. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 1(G1), 1–7.Google Scholar