한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제37권5호
  • /
  • Pages.367-377
  • /
  • 2019
  • /
  • 1598-4850(pISSN)
  • /
  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
권호 표지
DOI QR코드

DOI QR Code

Development and Performance Analysis of a New Navigation Algorithm by Combining Gravity Gradient and Terrain Data as well as EKF and Profile Matching

  • 투고 : 2019.09.30
  • 심사 : 2019.10.18
  • 발행 : 2019.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.

키워드

참고문헌

  1. Cowie, M., Wilkinson, N., and Powlesland, R. (2008), Latest developments of the TERPROM(R) digital terrain system (DTS), Proceedings of IEEE/ION PLANS 2008, ION, 6-8 May, Monterey, CA, USA, pp. 1219-1229.
  2. Cox, D.B. (1978), Integration of GPS with inertial navigation systems, Navigations, Vol. 25, No. 2, pp. 236-245. https://doi.org/10.1002/j.2161-4296.1978.tb01335.x
  3. Dai, T., Miao, L., and Shao, H. (2019), A robust underwater navigation method fusing data of gravity anomaly and magnetic anomaly, Inernational Journal of Systems Science, Vol. 50, No. 4, pp679-693. https://doi.org/10.1080/00207721.2019.1567866
  4. DeGregoria, A. (2010), Gravity Gradiometry and Map Matching: An Aid to Aircraft Inertial Navigation Systems, Master's thesis, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH, USA, 130p.
  5. Groves, P.D. (2013), Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Artech house, Boston, MA, USA.
  6. Hollowell, J. (1990), HELI/SITAN: a terrain referenced navigation algorithm for helicopter, Proceedings of IEEE/ION PLANS 1990, ION, 20-23 March, Las Vegas, NV, USA, pp. 616-625.
  7. Laur, T.M. and Llanso, S.L. (1995), Encyclopedia of Modern U.S. Military Weapons, The Army Times Publishing Company with Berkley Publishing Group, New York, N.Y., USA.
  8. Lee. B. and Kwon, J.H. (2010), Terrain referenced navigation simulation using area-based matching method and TERCOM, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 28, No. 1, pp. 73-82. (in Korean with English abstract)
  9. Lee, D., Kim, Y., and Bang, H. (2013), Vision-based terrain referenced navigation for unmanned aerial vehicles using homography relationship, Journal of Intelligent & Robotic Systems, Vol. 69, No. 1, pp. 489-497. https://doi.org/10.1007/s10846-012-9750-1
  10. Lee, J., Kwon, J.H., and Yu. M. (2014), Development of gravity gradient referenced navigation and its horizontal accuracy analysis, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 1, pp. 63-73. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2014.32.1.63
  11. Lee, J., Kwon, J.H., and Yu, M. (2015), Performance evaluation and requirements assessment for gravity gradient referenced navigation, Sensors, Vol. 15, No. 7, pp. 16833-16847. https://doi.org/10.3390/s150716833
  12. Lee, J. and Kwon, J.H. (2016), Analysis of database referenced navigation by the combination of heterogeneous geophysical data and algorithms, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 34, No. 4, pp. 373-382. https://doi.org/10.7848/ksgpc.2016.34.4.373
  13. Lee, W., Yoo, Y.M., Yun, S., and Park, C.G. (2014), Weighting logic design of hybrid database referenced navigation algorithm using multiple geophysical information, Proceedings of 19th IFAC World Congress, IFAC, 24-29 August, Cape Town, South Africa, Vol. 47, No. 3, pp. 3407-3412.
  14. Leonard, J.J. and Bahr, A. (2019), Autonomous Underwater Vehicle Navigation, In: (Dhanak, M.R. and Xiros, N.I (eds.), Springer Handbook of Ocean Engineering, Springer International Publishing, NY, USA, pp.341-358.
  15. Liu, F., Qian, D., Liu, F., and Li, Y. (2009), Integrated navigation system based on correlation between gravity gradient and terrain, Proceedings of International Joint Conference on Computational Sciences and Optimization, IEEE, 24-26 April, Hainan, China, Vol. 2, pp. 289-293.
  16. Liu, F., Qian, D., Zhang, Y., and Li, Y. (2010), A computer simulation of the influence of GGI and inertial sensors on gravity gradient aided navigation, Proceedings of 2010 International Symposium on Systems and Control in Aeronautics and Astronautics, ISSCAA, 8-10 June, Harbin, China, pp. 793-797.
  17. Mok, S.H., Band, H.C. and Yu, M.J. (2012), A performance comparison of nonlinear Kalman filtering based terrain referenced navigation, Journal of the Korean Society for Aeronautical and Space Science, Vol. 40, No. 2, pp.108-117. (in Korean with English abstract) https://doi.org/10.5139/JKSAS.2012.40.2.108
  18. Nielson, J.T., Swearingen, G.W., and Witzmeer, A.J. (1986), GPS aided inertial navigation, Aerospace and Electronic Systems Magazine, Vol. 1, No. 3, pp.20-26.
  19. Perea, L., How, J., Breger, L., and Elosegui, P. (2007), Nonlinearity in sensor fusion: divergence issues in EKF, modified truncated SOF, and UKF, Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit, AIAA, 20-23 August, Hilton Head, S.C., USA, Vol. 6514.
  20. Richeson, J.A. (2008), Gravity Gradiometer Aided Inertial Navigation within Non-GNSS Environments, Ph. D. dissertation, University of Maryland, College Park, MD, USA, 405p.
  21. Robins, A. (1998), Recent developments in the 'TERPROM' integrated navigation system, Proceedings of the ION 44th Annual Meeting, ION, 21-23 June, Annapolis, MD, USA, pp. 58-66.
  22. Rogers, M.M. (2009), An Investigation into the Feasibility of Using a Modern Gravity Gradient Instrument for Passive Aircraft Navigation and Terrain Avoidance, Master's thesis, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH, USA, 164p.
  23. Skog, I. (2009), Low-Cost Navigation Systems: A Study of Four Problems, Ph.D. dissertation, KTH Royal Institute of Technology, Stockholm, Sweden, 165p.
  24. Stutters, L., Liu, H., Tiltman, C., and Brown, D.J. (2008), Navigation technologies for autonomous underwater vehicles, IEEE Transactions on Systems, Man, and Cytermetrics, Part C, Vol. 38, No. 4, pp.581-589. https://doi.org/10.1109/TSMCC.2008.919147
  25. Wang, B., Yu, L., Deng, Z., and Fu, M. (2016), A particle filter-based matching algorithm with gravity sample vector for underwater gravity aided navigation, IEEE/ASME Transactions on Mechatronics, Vol. 21, No. 3, pp.1399-1408. https://doi.org/10.1109/TMECH.2016.2519925
  26. Wang, Z. and Bian, S. (2008), A local geopotential model for implementation of underwater passive navigation. Progress in Natural Science, Vol. 18, No. 9, pp. 1139-1145. https://doi.org/10.1016/j.pnsc.2008.02.011
  27. Xiong, L., Xiao, L.W., Dan, B.B., and Ma, J. (2013), Full tensor gravity gradient aided navigation based on nearest matching neural network, Proceedings of Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, IEEE, 21-25 July, Chengdu, China, pp. 462-465.
  28. Yu, Y.M., Yu, M.J., and Park, C.G. (2013), Research trend of terrain referenced navigation system, Institute of Control, Robotics and Systems, Vol. 19, No. 1, pp.19-31.
  29. Yurong, H., Bo, W., Zhihong, D., and Mengyin, F. (2018), Point mass filter based matching algorithm in gravity aided underwater navigation, Journal of Systems Engineering and Electonics, Vol. 29, No. 1, pp.152-159. https://doi.org/10.21629/JSEE.2018.01.15
  30. Zhang, F., Chen, X., Sun, M., Yan, M., and Yang, D. (2004), Simulation study of underwater passive navigation system based on gravity gradient, Proceedings of International Geoscience and Remote Sensing Symposium, IEEE, 20-24 September, Anchorage, AK, USA, Vol. 5, pp. 3111-3113.