Volume 4, Issue 3 (Fall 2020)                   ijcoe 2020, 4(3): 15-21 | Back to browse issues page

XML Print


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

Khalilabadi M R. Underwater Terrain and Gravity aided inertial navigation based on Kalman filter. ijcoe. 2020; 4 (3) :15-21
URL: http://ijcoe.org/article-1-227-en.html
Faculty of Naval Aviation, Malek Ashtar University of Technology
Abstract:   (247 Views)
In this paper, we present a new method for terrain and gravity aided navigation. Gravity aided navigation and terrain aided navigation are map aided navigation methods for correcting Inertial Navigation System (INS) errors of Autonomous Underwater Vehicles (AUV). Map aided navigation uses the information of the geophysical field maps. For achieve the highest accuracy and reliability two or three map aided navigation systems are combined. In this paper, we proposed a new method that simultaneously uses gravity map data and terrain map data. For maps data fusion we use a Kalman filter which its measurement equation defined based on gravity and terrain of the experiment area. The experimental results are encouraging.
Full-Text [PDF 981 kb]   (126 Downloads)    
Type of Study: Research | Subject: Coastal Engineering
Received: 2020/11/23 | Accepted: 2021/01/13 | ePublished: 2021/02/20

References
1. Paull, L., S. Saeedi, M. Seto and H. Li, 2013. AUV navigation and localization: A review. IEEE Journal of Oceanic Engineering, 39(1): 131-149. [DOI:10.1109/JOE.2013.2278891]
2. Allotta, B., A. Caiti, R. Costanzi, F. Fanelli, D. Fenucci, E. Meli and A. Ridolfi, 2016. A new AUV navigation system exploiting unscented Kalman filter. Ocean Engineering, 113: 121-132. [DOI:10.1016/j.oceaneng.2015.12.058]
3. Mu, X., J. Guo, Y. Song, Q. Sha, J. Jiang, B. He and T. Yan, 2017. Application of modified EKF algorithm in AUV navigation system, In OCEANS 2017-Aberdeen, IEEE, pp: 1-4. [DOI:10.1109/OCEANSE.2017.8084626]
4. Salavasidis, G., A. Munafo, C.A. Harris, S.D. McPhail, E. Rogers and A.B. Phillips, 2018. Towards arctic AUV navigation. IFAC-PapersOnLine, 51(29): 287-292. [DOI:10.1016/j.ifacol.2018.09.517]
5. Franchi, M., A. Ridolfi and M. Pagliai, 2020. A forward-looking SONAR and dynamic model-based AUV navigation strategy: Preliminary validation with FeelHippo AUV. Ocean Engineering, 196: 106770. [DOI:10.1016/j.oceaneng.2019.106770]
6. Kamgar-Parsi, B. and B. Kamgar-Parsi, 1999. Vehicle localization on gravity maps, In Unmanned Ground Vehicle Technology, International Society for Optics and Photonics, pp: 182-191. [DOI:10.1117/12.354447]
7. Wu, L., J. Ma and J. Tian, 2010. A self-adaptive unscented Kalman filtering for underwater gravity aided navigation, In IEEE/ION Position, Location and Navigation Symposium, IEEE, pp: 142-145. [DOI:10.1109/PLANS.2010.5507294]
8. Wang, H., L. Wu, H. Chai, H. Hsu and Y. Wang, 2016. Technology of gravity aided inertial navigation system and its trial in South China Sea. IET Radar, Sonar & Navigation, 10(5): 862-869. [DOI:10.1049/iet-rsn.2014.0419]
9. Kuang, J., X. Niu, P. Zhang and X. Chen, 2018. Indoor positioning based on pedestrian dead reckoning and magnetic field matching for smartphones. Sensors, 18(12): 4142. [DOI:10.3390/s18124142]
10. Li, M., Y. Liu and L. Xiao, 2014. Performance of the ICCP algorithm for underwater navigation, In 2014 International Conference on Mechatronics and Control (ICMC), IEEE, pp: 361-364. [DOI:10.1109/ICMC.2014.7231579]
11. Wang, H., X. Xu and T. Zhang, 2018. Multipath parallel ICCP underwater terrain matching algorithm based on multibeam bathymetric data. IEEE Access, 6: 48708-48715. [DOI:10.1109/ACCESS.2018.2866687]
12. Bishop, G.C., 2002. Gravitational field maps and navigational errors [unmanned underwater vehicles]. IEEE Journal of Oceanic Engineering, 27(3): 726-737. [DOI:10.1109/JOE.2002.1040954]
13. Zhang, H., L. Yang and M. Li, 2019. Improved ICCP algorithm considering scale error for underwater geomagnetic aided inertial navigation. Mathematical Problems in Engineering, 2019:. [DOI:10.1155/2019/1527940]
14. Wu, M. and J. Yao, 2015. Adaptive UKF-SLAM based on magnetic gradient inversion method for underwater navigation, In 2015 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, pp: 839-843. [DOI:10.1109/ICUAS.2015.7152369]
15. Melo, J. and A. Matos, 2017. Survey on advances on terrain based navigation for autonomous underwater vehicles. Ocean Engineering, 139: 250-264. [DOI:10.1016/j.oceaneng.2017.04.047]
16. Bozorg, M., M.S. Bahraini and A.B. Rad, 2019. New Adaptive UKF Algorithm to Improve the Accuracy of SLAM. International Journal of Robotics, Theory and Applications, 5(1): 35-46.
17. Deng, Z., Y. Ge, W. Guan and K. Han, 2010. Underwater map-matching aided inertial navigation system based on multi-geophysical information. Frontiers of Electrical and Electronic Engineering in China, 5(4): 496-500. [DOI:10.1007/s11460-010-0098-7]
18. Zheng, H., H. Wang, L. Wu, H. Chai and Y. Wang, 2013. Simulation research on gravity-geomagnetism combined aided underwater navigation. The Journal of Navigation, 66(1): 83-98. [DOI:10.1017/S0373463312000343]
19. Wang, H., L. Wu, H. Chai, Y. Xiao, H. Hsu and Y. Wang, 2017. Characteristics of marine gravity anomaly reference maps and accuracy analysis of gravity matching-aided navigation. Sensors, 17(8): 1851. [DOI:10.3390/s17081851]
20. Wang, C., B. Wang, Z. Deng and M. Fu, 2020. A Delaunay Triangulation Based Matching Area Selection Algorithm for Underwater Gravity-Aided Inertial Navigation. IEEE/ASME Transactions on Mechatronics. [DOI:10.1109/TMECH.2020.3012499]
21. Bao, J., D. Li, X. Qiao and T. Rauschenbach, 2020. Integrated navigation for autonomous underwater vehicles in aquaculture: A review. Information Processing in Agriculture, 7(1): 139-151. [DOI:10.1016/j.inpa.2019.04.003]
22. Michalski, J., P. Kozierski and J. Zińôtkiewicz, 2019. The new approach to hybrid Kalman filtering, based on the changed order of filters for state estimation of dynamical systems. Poznan University of Technology Academic Journals. Electrical Engineering. [DOI:10.1051/itmconf/20192801051]
23. Cummins, D.P., D.B. Stephenson and P.A. Stott, 2020. A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series. Advances in Statistical Climatology, Meteorology and Oceanography, 6(2): 91-102. [DOI:10.5194/ascmo-6-91-2020]
24. Meslem, N. and N. Ramdani, 2020. A new approach to design set-membership state estimators for discrete-time linear systems based on the observability matrix. International Journal of Control, 93(11): 2541-2550. [DOI:10.1080/00207179.2019.1628296]
25. Masnadi-Shirazi, H., A. Masnadi-Shirazi and M.-A. Dastgheib, 2019. A Step by Step Mathematical Derivation and Tutorial on Kalman Filters. ArXiv Preprint ArXiv:1910.03558.
26. Wu, L., H. Wang, H. Chai, H. Hsu and Y. Wang, 2015. Research on the relative positions-constrained pattern matching method for underwater gravity-aided inertial navigation. The Journal of Navigation, 68(5): 937-950. [DOI:10.1017/S0373463315000235]
27. Wei, E., C. Dong, J. Liu, Y. Yang, S. Tang, G. Gong and Z. Deng, 2017. A robust solution of integrated SITAN with TERCOM algorithm: weight-reducing iteration technique for underwater vehicles' gravity-aided inertial navigation system. NAVIGATION, Journal of the Institute of Navigation, 64(1): 111-122. [DOI:10.1002/navi.176]
28. Wu, L., J. Gong, H. Cheng, J. Ma and J. Tian, 2007. New method of underwater passive navigation based on gravity gradient, In MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, International Society for Optics and Photonics, p: 67901V. [DOI:10.1117/12.749408]
29. Wu, L. and J. Tian, 2010. Automated gravity gradient tensor inversion for underwater object detection. Journal of Geophysics and Engineering, 7(4): 410-416. [DOI:10.1088/1742-2132/7/4/008]
30. Mashayekhpour, M., R. Emadi and M. Torabi Azad, 2018. Investigation on the Seasonal Variations of Tidal Constituents in the North Coasts of Persian Gulf and Oman Sea. Hydrophysics, 2(2): 67-77.
31. Hosseini hamid, M. and M. Akbarinasab, 2016. The Calculation of the Optimum Index Factor for Monitoring Water Resources pollution using Satellite Images: A Case Study of the Oman sea. Hydrophysics, 2(1): 35-45.
32. ghazi, E., M. Ezam, A. Aliakbari Bidokhti, M. Torabi Azad and E. Hasanzade, 2018. Modeling Thermohaline Front of the Persian Gulf Outflow in the Oman Sea. Hydrophysics, 4(1): 1-17.
33. yazdanfar, salar, A. Amir Ashtari Larki, mohammad akbarinasab and A. Delbari, 2018. Study of surface fronts in the Oman Sea. Hydrophysics, 4(1): 19-31.
34. rahnemania, abdossamad, A.A. Aliakbari Bidokhti, M. Ezam, K. Lari and S. Ghader, 2019. The Role of Bottom Friction on the Changes of Salinity Front in the Persian Gulf. Hydrophysics, 4(2): 15-25.
35. Lubis, F.F., Y. Rosmansyah and S.H. Supangkat, 2014. Gradient descent and normal equations on cost function minimization for online predictive using linear regression with multiple variables, In 2014 International Conference on ICT For Smart Society (ICISS), IEEE, pp: 202-205. [DOI:10.1109/ICTSS.2014.7013173]
36. Shanthamallu, U.S., A. Spanias, C. Tepedelenlioglu and M. Stanley, 2017. A brief survey of machine learning methods and their sensor and IoT applications, In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE, pp: 1-8. [DOI:10.1109/IISA.2017.8316459]
37. Pillai, A.S., G.S. Chandraprasad, A.S. Khwaja and A. Anpalagan, 2019. A service oriented IoT architecture for disaster preparedness and forecasting system. Internet of Things, 100076. [DOI:10.1016/j.iot.2019.100076]
38. Singh, S. and M. St-Hilaire, 2020. Prediction-Based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers. Communications and Network, 12(02): 74. [DOI:10.4236/cn.2020.122005]

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

Send email to the article author


© 2021 All Rights Reserved | International Journal of Coastal and Offshore Engineering