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A Triangle Matching Algorithm for Gravity-aided Navigation for Underwater Vehicles

Published online by Cambridge University Press:  17 October 2013

Zhenli Yang ,
Zhuangsheng Zhu  and
Weigao Zhao
Zhenli Yang*
Affiliation:
(Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument & Navigation System Technology, Beihang University, Beijing, China)
Zhuangsheng Zhu
Affiliation:
(Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument & Navigation System Technology, Beihang University, Beijing, China)
Weigao Zhao
Affiliation:
(Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument & Navigation System Technology, Beihang University, Beijing, China)
*
(E-mail: 617188138@qq.com)
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Abstract

In this paper, a triangle matching algorithm using local gravity field maps is proposed to bound the drift errors inherent in Strapdown Inertial Navigation Systems (SINS) in gravity-aided navigation. This triangle matching algorithm has two main stages, the first is the initial matching stage, which has a coarse phase and a fine phase to address the large unknown initial errors made by INS, and the other is the tracking matching stage, which mainly aims at tracking the matching solution with the vehicle running in real time. Simulations were carried out using data for the Bohai Sea and South China Sea areas, to assess the effects of different initial errors on the matching solutions. Finally some experiments were carried out to evaluate the proposed algorithm. The results show that the triangle matching algorithm has some compelling advantages, such as a capability to address the large unknown initial errors made by INS, and good real-time quality of matching the gravity measurements with the local gravity maps.

Keywords

Gravity-aided navigationINS navigationTriangle matching algorithm
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 2 , March 2014 , pp. 227 - 247
Copyright
Copyright © The Royal Institute of Navigation 2013 

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References

REFERENCES

Anthony, J.H. and David, L. (1993). Multivariable Sliding-mode Control for Autonomous Diving and Steering of Unmanned Underwater Vehicles. IEEE Journal of Oceanic Engineering, 18, 327–339.Google Scholar
Behzad, K.P. and Behrooz, K.P. (1999). Vehicle Localization of Gravity Maps. Proc. SPIE Conf. Unmanned Ground Vehicle Technology, 3693, 182–191.Google Scholar
Cheng, J.H., Shi, J.Y., Wang, X.Z. and Dong, J.M. (2010). The Research of INS's Error Analysis for Underwater Vehicle in Complex Sea Conditions. The 2010 8th World Congress on Intelligent Control and Automation (WCICA), Jinan, CN.CrossRefGoogle Scholar
Cheng, Y., Xiu, C.B. and Luo, J. (2009). The Simulation of ICCP Algorithm in the Gravity Aided Navigation. The Second International Conference on Intelligent Networks and Intelligent Systems, Tianjin, CN.Google Scholar
Garner, C.B. (2002). Gravity Field Maps and Navigation Errors. IEEE Journal of Oceanic Engineering, 27, 726–737.Google Scholar
Guo, X.J. and Cao, X.C. (2012). Good Match Exploration Using Triangle Constraint. Pattern Recognition Letters, 22, 872–881.CrossRefGoogle Scholar
Gyung, N.J. and Hang, S.C. (2006). Velocity-aided Underwater Navigation System Using Receding Horizon Kalman Filter. IEEE Journal of Oceanic Engineering, 31, 565–573.Google Scholar
Heung, W.P., Jang, G.L. and Chan, G.P. (2002). Covariance Analysis of Strapdown INS Considering Gyrocompass Characteristics. The IEEE Transactions on Aerospace and Electronic Systems, 31, 320–328.Google Scholar
Hugh, R., Louis, M., Rovert, A. and Daniel, M. (2000). Next Generation Marine Precision Navigation System. Position Location and Navigation Symposium, San Diego, CA.Google Scholar
Ingemar, N. and Magnus, J. (2004). Terrain Navigation for Underwater Vehicles Using the Correlator Method. IEEE Journal of Oceanic Engineering, 29, 906–915.Google Scholar
Larry, D. and Ronald, D. (1983). Nonlinear Kalman Filtering Techniques for Terrain-aided Navigation. IEEE Transactions on Automatic Control, 28, 315–323.Google Scholar
Li, J., Bian, X.Q., Shi, X.C. and Qin, Z. (2007). Simulation System of Gravity Aided Navigation for Autonomous Underwater Vehicle. Proceedings of the 2007 IEEE, International Conference on Mechatronics and Automatics, Harbin, CN.Google Scholar
Lin, Y., Yan, L. and Tong, Q.X. (2007). Underwater Geomagnetic Navigation Based on ICP Algorithm. The Proceedings of the 2007 IEEE, International Conference on Robotics and Biomimetics, Beijing, CN.Google Scholar
Liu, F.M., Li, Y., Zhang, Y.F. and Hou, H.J. (2011). Application of Kalman Filter Algorithm in Gravity-aided Navigation System. The Proceedings of the 2011 IEEE, International Conference on Mechatronics and Automation, Harbin, CN.CrossRefGoogle Scholar
Liu, Y. and Tan, Z.F. (2011). Research and Design of Terrain Aided Navigation System. The 7th International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, CN.Google Scholar
Liu, Z.X. and An, J.B. (2010). A New Algorithm of Global Feature Matching Based on Triangle Regions for Image Registration. The 10th International Conference on Signal Processing (ICSP), Dalian, CN.Google Scholar
Panagiotopoulou, A. and Anastassopoulos, V. (2007). Super-Resolution Image Reconstruction Employing Kriging Interpolation Technique. The 2007 and 6th EURASIP Conference on Systems, Signals and Image Processing, Maribor, SL.Google Scholar
Raty, M. and Kangas, A. (2012). Comparison of k-MSN and Kriging in Local Prediction. Forest Ecology and Management, 263, 47–56.Google Scholar
Sun, W.B., Shan, S.G., Chen, F. and Zhu, L.C. (2010). Geometry-based Mapping of Vector Data and DEM Based on Hierarchical Longitude/Latitude Grids. The 2010 second IITA International Conference on Geoscience and Remote Sensing (IITA-GRS), Qingdao, CN.Google Scholar
Wang, T., Dong, W.Q. and Wang, P. (2008). Time Series Marching Algorithm with Confidence and Error Bounds in WSNs. The Journal of Chinese Computer Systems, 6, 1027–1030.Google Scholar
Wang, Z.G. and Bian, S.F. (2008). A Local Geopotential Model for Implementation of Underwater Passive Navigation. Progress in Natural Science, 18, 1139–1145.Google Scholar
Yuan, G.N., Zhang, H.W., Yuan, K.F. and Tao, C.Y. (2011). A Combinational Underwater Aided Navigation Algorithm Based on TERCOM/ICCP and Kalman Filter. The 2011 Fourth International Joint Conference on Computational Sciences and Optimization, Nanchang, CN.Google Scholar
Zhang, F.Z., Chen, X.W., Sun, M. and Yan, M. (2011). Simultaneous Localization and Mapping Based on Multilevel-EKF. The 2011 International Conference on Mechatronics and Automation (ICMA), Beijing, CN.Google Scholar
Zhao, J.H., Wang, S.P. and Wang, A.X. (2009). Study on Underwater Navigation System Based on Geomagnetic Match Technique. The 9th International Conference on Electronic Measurement & Instruments, Wuhan, CN.Google Scholar
Zheng, J.D. and Gao, Y. (2009). Fingerprint Matching Algorithm Based on Similar Vector Triangle. The 2nd International Congress on Image and Signal Processing, Xiamen, CN.Google Scholar