Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-22T22:19:52.723Z Has data issue: false hasContentIssue false
  • English
  • Français

Underwater Terrain Positioning Method Using Maximum a Posteriori Estimation and PCNN Model

Published online by Cambridge University Press:  04 March 2019

Pengyun Chen ,
Pengfei Zhang ,
Teng Ma ,
Peng Shen ,
Ye Li ,
Rupeng Wang ,
Yue Han  and
Lizhou Li
Pengyun Chen*
Affiliation:
(College of Mechatronic Engineering, North University of China, Taiyuan 030051, China)
Pengfei Zhang
Affiliation:
(College of Mechatronic Engineering, North University of China, Taiyuan 030051, China)
Teng Ma
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Peng Shen
Affiliation:
(National Deep Sea Center, Qingdao 266237, China)
Ye Li
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Rupeng Wang
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Yue Han
Affiliation:
(Modern Education Information Centre, Taiyuan Tourism College, Taiyuan 030032, China)
Lizhou Li
Affiliation:
(College of Mechatronic Engineering, North University of China, Taiyuan 030051, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Conventional underwater navigation and positioning methods for Autonomous Underwater Vehicles (AUVs) either require the installation of acoustic arrays, which make AUVs less independent, or result in cumulative errors. This paper proposes an Underwater Terrain Positioning Method (UTPM) using Maximum a Posteriori (MAP) estimation and a Pulse Coupled Neural Network (PCNN) model for highly accurate navigation by AUVs. The PCNN model is used as a secondary discriminant to effectively identify pseudo-anchor points in flat terrain feature areas and to find the true positioning point, which significantly improves the matching positioning accuracy in these areas. Simulation results show that the proposed method effectively corrects Inertial Navigation System (INS) cumulative errors and has high matching positioning accuracy, which satisfy the requirements of AUV underwater navigation and positioning.

Keywords

Autonomous Underwater VehicleTerrain Matching PositioningMaximum a Posteriori estimationPulse Coupled Neural Network
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 5 , September 2019 , pp. 1233 - 1253
Copyright
Copyright © The Royal Institute of Navigation 2019 

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

Bergman, N. and Ljung, L. (2009). Point-mass filter and Cramer-Rao bound for terrain-aided navigation. Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. Wiley-IEEE Press, 850–855.Google Scholar
Chang, Q., Yang, D. K., Kou, Y. H. and Zhang, Q. S. (2005). Vehicle Navigation Positioning Method and Application. Mechanical Industry Press.Google Scholar
Chen, P. Y., Li, Y., Su, Y., Chen, X. and Jiang, Y. (2015). Review of AUV underwater terrain matching navigation. The Journal of Navigation, 68(6), 1155–1172.Google Scholar
Chen, X. L. (2013). A study on underwater terrain matching aided navigation technology of AUV. PhD Thesis, Harbin Engineering University.Google Scholar
Claus, B. and Bachmayer, R. (2015). Terrain-aided navigation for an underwater glider. Journal of Field Robotics, 32(1), 935–951.Google Scholar
Ding, J. L. and Xiao, J. (2014). Design of adaptive cubature Kalman filter based on maximum a posteriori estimation. Control and Decision, 29(2), 327–334.Google Scholar
Eckhorn, R., Reitboeck, H. J., Arndt, M. and Dicke, P. (1990). Feature linking via synchronization among distributed assemblies: Simulation of results from cat cortex. Neural Computation, 2, 293–307.Google Scholar
Geisser, S. (1992). Introduction to Fisher (1922) On the mathematical foundations of theoretical statistics. In: Kotz, S., Johnson, N.L. eds., Breakthroughs in Statistics. Springer Series in Statistics (Perspectives in Statistics). New York: Springer, 1–10Google Scholar
GeoAcoustics Limited. (2007). GeoSwath Plus Operation Manual, GeoAcoustics Limited. UK.Google Scholar
Hagen, O. K. and Anonsen, K. B. (2014). Using terrain navigation to improve marine vessel navigation systems. Marine Technology Society Journal, 48(2), 45–58.Google Scholar
Hagen, O. K., Anonsen, K. B. and Saebo, T. O. (2012). Low-altitude terrain navigation for underwater vehicles integration of an interferometric side scan sonar improves terrain navigation in low-altitude scenarios. Sea Technology, 53(6), 10–13.Google Scholar
Ji, D. and Liu, J. (2010). Ray theory application in long baseline system. China Ocean Engineering, 24(1), 199–206.Google Scholar
Lee, H. (2016). Optimization of computation efficiency in underwater acoustic navigation system. The Journal of the Acoustical Society of America, 139(4), 1909–1913.Google Scholar
Li, Y., Chen, P. Y. and Dong, Z. P. (2011). Sensor simulation of underwater terrain matching based on sea chart. Communications in Computer and Information Science, 216, 89–94.Google Scholar
Li, Y., Ma, T., Chen, P. Y., Jiang, Y. Q., Wang, R. and Zhang, Q. (2017). Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation. Ocean Engineering, 133(133), 107–115.Google Scholar
Lindblad, T. and Kinser, J. (2013). Image Processing using pulse-coupled neural networks: Applications in Python. Springer Science and Business Media.Google Scholar
Mohammed, M. M., Badr, A. and Abdelhalim, M. B. (2015). Image classification and retrieval using optimized pulse-coupled neural network. Expert Systems with Applications, 42(2015), 4927–4936.Google Scholar
Morgado, M., Oliveira, P. and Silvestre, C. (2013). Tightly coupled ultrashort baseline and inertial navigation system for underwater vehicles: An experimental validation. Journal of Field Robotics, 30(1), 142–170.Google Scholar
Nordlund, P. J. and Gustafsson, F. (2010). marginalized particle filter for accurate and reliable terrain-aided navigation. IEEE Transactions on Aerospace & Electronic Systems, 45(4), 1385–1399.Google Scholar
Nygren, I. (2005). Terrain Navigation for Underwater Vehicles. PhD Thesis of the Royal Institute of Technology.Google Scholar
Nygren, I. (2008). Robust and efficient terrain navigation of underwater vehicles. Proceedings of IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, USA, 923–932.Google Scholar
Pan, X. H. and Zhao, L. (2015). Application of an unscented Kalman filter algorithm in the seabed terrain aided navigation. Applied Science and Technology, 42(1), 49–52.Google Scholar
Paull, L., Saeedi, S. and Seto, M. (2014). AUV navigation and localization: A review. IEEE Journal of Oceanic Engineering, 39(1), 131–149.Google Scholar
Peng, D. D., Zhou, T., Li, H. S. and Zhang, W. Y. (2016). Terrain aided navigation for underwater vehicles using maximum likelihood method. Proceedings of 2016 IEEE/OES China Ocean Acoustics Symposium, Harbin, China, 1–6.Google Scholar
Teixeira, F. C., Quintas, J., Maurya, P. and Pascoal, A. (2017). Robust particle filter formulations with application to terrain-aided navigation. International Journal of Adaptive Control and Signal Processing, 31(4), 608–651.Google Scholar
Wang, H., Yan, L., Qian, X. and Zhu, M. (2007). Integration terrain match algorithm based on terrain entropy and terrain variance entropy. Computer Technology and Development, 17(9), 25–27.Google Scholar
Xie, Y.R. (2005). Terrain Aided Navigation. MSc Thesis, the Royal Institute of Technology.Google Scholar
Xing, T. H. (2004). The research of terrain-aided underwater navigation. MSc Thesis, Northwest Polytechnical University.Google Scholar
Xu, Y. R., Pang, Y. J., Gan, Y. and Sun, Y. S. (2006). AUV—State of the art and prospect. CAAI Transactions on Intelligent Systems, 1(1), 9–16.Google Scholar
Yan, Z. P., Peng, S. P., Zhou, J. J., Xu, J. and Jia, H. (2010). Research on an improved dead reckoning for AUV navigation. Proceedings of 2010 Chinese Control and Decision Conference, Xuzhou, Jiangsu, China, 1794–1798.Google Scholar
Yao, Y. B., Hu, M. X. and Xu, C. Q. (2016). Positioning accuracy analysis of GPS/BDS/GLONASS network RTK based on DREAMNET. Acta Geodaeticaet Cartographica Sinica, 45(9), 1009–1018.Google Scholar
Zhao, L., Gao, N., Huang, B., Wang, Q. and Zhou, J. (2015). A Novel Terrain-Aided Navigation Algorithm Combined with the TERCOM Algorithm and Particle Filter. IEEE Sensors Journal, 15(2), 1124–1131.Google Scholar