Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T18:33:16.375Z Has data issue: false hasContentIssue false
  • English
  • Français

Terrain Matching Positioning Method Based on Node Multi-information Fusion

Published online by Cambridge University Press:  08 July 2016

Ye Li ,
Rupeng Wang ,
Pengyun Chen ,
Peng Shen  and
Yanqing Jiang
Ye Li
Affiliation:
(Science and Technology on Underwater Vehicles laboratory, Harbin Engineering University, 150001, China)
Rupeng Wang*
Affiliation:
(Science and Technology on Underwater Vehicles laboratory, Harbin Engineering University, 150001, China)
Pengyun Chen
Affiliation:
(Science and Technology on Underwater Vehicles laboratory, Harbin Engineering University, 150001, China)
Peng Shen
Affiliation:
(Science and Technology on Underwater Vehicles laboratory, Harbin Engineering University, 150001, China)
Yanqing Jiang
Affiliation:
(Science and Technology on Underwater Vehicles laboratory, Harbin Engineering University, 150001, China)
*
(E-mail: wangrupeng@hrbeu.edu.cn)
Get access Rights & Permissions [Opens in a new window]

Abstract

Measurement bias and lack of terrain features often cause false peaks during underwater terrain matching positioning, that is, there is more than one peak near the real position. Previous methods to address this problem have increased the number of measurement beams, but this also increases the data processing time and energy consumption. At the same time, the ratio of measured information that is used does not increase. In other words, we should increase the ratio of measured information that is used, not simply increase the amount of information that is measured. Conventional matching algorithms only use the height of nodes without considering surface information, which is composed of height and the position of multiple nodes in three-dimensional space. Multi-beam sonar can obtain the three-dimensional distribution of terrain nodes. This node information is not just a height sequence, as it is used in previous methods. If we consider the nodes as a three-dimensional distribution of points with height and position information, this increases the matching position information and more of the terrain features can be extracted from the same measured data. Hence, in this paper, a terrain positioning method called the Node Multi-information Fusion (NMIF) is presented. This method focuses on improving the stability and accuracy degraded by bias in the Digital Elevation Map (DEM), terrain repeatability, and other factors. First, the concept of a Single Node Data Packet (SNDP) is introduced. The SNDP includes elevation and surface information surrounding the node, such as roughness, gradient, and slope. This additional topographic feature information improves the robustness and accuracy of the system. A computer simulation using actual ocean bottom topography verifies the advantages of the proposed NMIF algorithm.

Keywords

Autonomous Underwater Vehicle (AUV)Terrain reference navigationNode data packetMulti-information fusion
Type
Review Article
Information
The Journal of Navigation , Volume 70 , Issue 1 , January 2017 , pp. 82 - 100
Copyright
Copyright © The Royal Institute of Navigation 2016 

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

Anonsen, K.B. and Hagen, O.K. (2010). An analysis of real-time terrain aided navigation results from a HUGIN AUV. OCEANS 2010, Seattle, WA.Google Scholar
Carreno, S., Wilson, P., Ridao, P. and Petillot, Y. (2010). A survey on Terrain Based Navigation for AUVs. OCEANS, Seattle, WA.Google Scholar
Chen, X. (2013). A Study on Underwater Terrain Matching Aided Navigation Technology of AUV. Doctoral Thesis in Design and Construction of Naval Architecture and Ocean Structure, Harbin Engineering University, Harbin, China (in Chinese).Google Scholar
Hagen, O.K. (2006). TerrLab–a generic simulation and post-processing tool for terrain referenced navigation. OCEANS, Boston, MA.Google Scholar
Hagen, O.K., Ånonsen, K.B. and Mandt, M. (2010). The HUGIN Real-Time Terrain Navigation System. OCEANS 2010, Seattle, WA.Google Scholar
Liu, C. (2003). Underwater Vehicle Terrain Reference Positioning Technology Research. Doctoral Thesis in Navigation, Guidance and Control, Harbin Engineering University, Harbin, China.Google Scholar
Meduna, D.K., Rock, S.M. and McEwen, R. (2010). Closed-loop terrain relative navigation for AUVs with non-inertial grade navigation sensors. Autonomous Underwater Vehicles, IEEE/OES, Monterey, CA. IEEE.CrossRefGoogle Scholar
Meduna, D.K., Rock, S.M. and McEwen, R. (2008). Low-cost terrain relative navigation for long-range AUVs. Oceans, Quebec City, QC. IEEE.CrossRefGoogle Scholar
Nakatani, T., Ura, T., Sakamaki, T. and Kojima, J. (2009). Terrain based localization for pinpoint observation of deep seafloors. Oceans, Bremen, Germany.CrossRefGoogle Scholar
Nygren, I. (2005). Terrain Navigation for Underwater Vehicles. Doctoral Thesis in Signal Processing, Stockholm, Sweden.Google Scholar
Nygren, I. (2008). Robust and Efficient Terrain Navigation of Underwater Vehicles. Position, Location and Navigation Symposium, IEEE/ION, 1–3, 923–932.Google Scholar
Shi, Y. (2003). Probability theory and mathematical statistics. Harbin: Department of Applied Mathematics, Harbin Engineering University (in Chinese).Google Scholar
Sun, X. (2002). Modern Pattern Recognition. Changsha: National Defense University Press (in Chinese).Google Scholar
Tian, F. (2007). Research on Prior Map Data Processing Based Terrain Aided Navigation Methods for Underwater vehicle. Master Degree Thesis of Engineering, Harbin Engineering University, Harbin, China (in Chinese).Google Scholar
Xie, Y.G. (2005). Terrain aided navigation. Master of Science Thesis, Stockholm, Sweden.Google Scholar
Zhang, F. Si, W., Yan, L. and Ge, Y. (2003). Simulation Research on Matching Algorithm of Autonomous Underwater Navigation System. Geomatics and Information Science of Wuhan University, 4:28(2) (in Chinese).Google Scholar
Zhang, H., Zhao, J. and Shao, N. (2011). Underwater Navigation Method based on Characteristics of The Seabed Topography. Control Technology, 30(11), 96–102 (in Chinese).Google Scholar
Zhang, K. Zhao, J. and Chen, Z. (2014). Research on Underwater Terrain Matching Navigation Based on Correlation. Journal Of Geodesy And Geodynamics, 2:34(1) (in Chinese).Google Scholar
Zhang, W. and Yang, X. (2004). Fuzzy Control Theory and Application. Northwestern Polytechnical University (in Chinese).Google Scholar
Zhang, Y., Liu, Y. and Ji, Z. (2009). Vector similarity measurement method. Technical Acoustics, 8: 28(4) (in Chinese).Google Scholar