Abstract
Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed.
There is today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques, most often relying on high-resolution maps, with the accuracy of the navigational sensors being less important.
Instead of relying on high-resolution maps and low accuracy navigation sensors, this paper presents an implementation of the opposite, namely using low-resolution maps, but compensating this by using high-accuracy navigational sensors and fusing data from both bottom depth measurements and magnetic field measurements. A Particle Filter uses the data to estimate a position, and as a second step, a Kalman Filter enhances the accuracy even further.
The algorithm has been tuned and evaluated using both a medium and a high-accuracy Inertial System. Comparisons of the various tuning methods are presented along with their performance results. The results from the simulated tests, described in this paper, show that for the high-end Inertial System, the mean position error is 10.2 m, and the maximum position error is 33.0 m during a 20 h test, which in most cases would be accurate enough to use for navigation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Humphreys, T.E., Ledvina, B.M., Psiaki, M.L., O’Hanlon, B.W., Kintner Jr., P.M.: Cornell: assessing the spoofing threat: development of a portable GPS civilian spoofer. In: Proceedings of the ION GNSS International Technical Meeting of the Satellite Division, vol. 55 (2008)
Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, U., Karlsson, R., Nordlund, P.-J.: Particle Filters for Positioning, Navigation, and Tracking. IEEE Trans. Signal Process. 50(2), 425–437 (2002). https://doi.org/10.1109/78.978396
Nygren, I.: Terrain navigation for underwater vehicles. In Ph.D. dissertation, Dept. Electr. Eng., Royal Institute of Technology (KTH), Sweden (2005)
Karlsson, R., Gustafsson, F.: Particle filter for underwater terrain navigation. In: IEEE Workshop on Statistical Signal Processing, pp. 526–529, (2003). https://doi.org/10.1109/SSP.2003.1289507
Ånonsen, K.B., Hallingstad, O.: Sigma point Kalman filter for underwater terrain-based navigation. In: IFAC Proceedings, vol. 40(17), pp. 106–110 (2007). https://doi.org/10.3182/20070919-3-HR-3904.00020
Nordlund, P.J.: Sequential Monte Carlo filters and integrated navigation. Ph.D. dissertation, Dept. Electr. Eng., Linköping University, Linköping (2002)
Karlsson, R., Gustafsson, F.: Bayesian surface and underwater navigation. IEEE Trans. Signal Process. 54(11), 4204–4213 (2006). https://doi.org/10.1109/TSP.2006.881176
Karlsson, R., Gustafsson, F., Karlsson, T.: Particle filtering and Cramer-Rao lower bound for underwater navigation. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal, vol. 6, pp. 65–68. IEEE (2003)
Donovan, G.T.: Position error correction for an autonomous underwater vehicle inertial navigation system (INS) using a particle filter. IEEE J. Oceanic Eng. 37(3), 431–445 (2012)
Nakatani, T., Ura, T., Sakamaki, T., Kojima, J.: Terrain based localization for pinpoint observation of deep seafloors. In: OCEANS 2009 - EUROPE, pp. 1–6 (2009). https://doi.org/10.1109/OCEANSE.2009.5278194
Fairfield, N., Wettergreen, D.: Active localization on the ocean floor with multibeam sonar. In: Proceedings of the OCEANS Conference, Quebec City, Canada (2008). https://doi.org/10.1109/OCEANS.2008.5151853
Ånonsen, K.B., Hallingstad, O.: Terrain aided underwater navigation using point mass and particle filters. In: 2006 IEEE/ION Position, Location, and Navigation Symposium, pp. 1027–1035. IEEE (2006)
Meduna, D.K., Rock, S.M., McEwen, R.: Low-cost terrain relative navigation for long-range AUVs. In: Proceedings of the OCEANS Conference, Quebec City, Canada (2008). https://doi.org/10.1109/OCEANS.2008.5152043
Carren, S., Wilson, P., Ridao, P., Petillot, Y.: A survey on terrain based navigation for AUVs. In: Proceedings of the OCEANS (2010). https://doi.org/10.1109/OCEANS.2010.5664372
Melo, J., Matos, A.: Survey on advances on terrain based navigation for autonomous underwater vehicles. In: Ocean Engineering, vol. 139, pp. 250-264 (2017). https://doi.org/10.1016/j.oceaneng.2017.04.047
Le Grand, E., Thrun, S.: 3-axis magnetic field mapping and fusion for indoor localization. In: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 358–364 (2012). https://doi.org/10.1109/MFI.2012.6343024
Frassl, M., Angermann, M., Lichstenstern, M., Robertson, P., Julian, B.J., Doniec, M.: Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 913–920 (2013). https://doi.org/10.1109/IROS.2013.6696459
Lager, M., Topp, E.A., Malec, J.: Underwater terrain navigation using standard sea charts and magnetic field maps. In: 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (2017). https://doi.org/10.1109/MFI.2017.8170410
Robertson, P., Angermann, M., Krach, B., Khider, M.: Inertial-based joint mapping and positioning for pedestrian navigation. In: Proceedings of the ION GNSS 2009 (2009)
Daellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo localization for mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 1999), pp. 1322–1338 (1999). https://doi.org/10.1109/ROBOT.1999.772544
Klein, L.A.: Sensor and Data Fusion - A Tool for Information Assessment and Decision Making. SPIE Press (2012). ISBN: 9780819491336
Acknowledgment
This work was partially supported by the Wallenberg Autonomous Systems and Software Program (WASP).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Lager, M., Topp, E.A., Malec, J. (2018). Underwater Terrain Navigation During Realistic Scenarios. In: Lee, S., Ko, H., Oh, S. (eds) Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System. MFI 2017. Lecture Notes in Electrical Engineering, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-319-90509-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-90509-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-90508-2
Online ISBN: 978-3-319-90509-9
eBook Packages: EngineeringEngineering (R0)