Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-28T05:21:12.916Z Has data issue: false hasContentIssue false

A Low Complexity Integrated Navigation System for Underwater Vehicles

Published online by Cambridge University Press:  09 May 2018

Mehdi Emami
Affiliation:
(Department of Electrical Engineering, Yazd University, Yazd, Iran)
Mohammad Reza Taban*
Affiliation:
(Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran)

Abstract

This paper proposes a simplified algorithm for reducing the computational load of the conventional underwater integrated navigation system. The system usually comprises a three-dimensional accelerometer, a three-dimensional gyroscope, a three-dimensional Doppler Velocity Log (DVL) and a data fusion algorithm, such as a Kalman Filter (KF). Since the expected variations of roll, pitch and depth are small, these quantities are assumed to be constant, and the proposed system is designed in a two-dimensional form. Due to the low speed of the vehicle, the nonlinear dynamic equation of the velocity can be simplified in a linear form. We also simplify the conventional KF in order to avoid matrix multiplications and matrix inversions. The performance of the designed system is evaluated in a sea trial by an Autonomous Underwater Vehicle (AUV). The results show that the proposed system can significantly reduce the computational load of the conventional integrated navigation system without a significant reduction in position and velocity accuracy.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2018 

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

Brandt, A. and Gardner, J.F. (1996). Constrained Navigation Algorithms for Strapdown Inertial Navigation Systems with Reduced Set of Sensors. Proceedings of the 25th Annual Technical Symposium of the International Loran Association, Lisbon, Portugal.Google Scholar
Farrell, J.A. (Ed.), 2008. Aided Navigation, GPS with High Rate Sensors. McGraw-Hill.Google Scholar
Forssell, B. (2008). Radionavigation Systems. Artech House.Google Scholar
Gao, W., Li, J., Zhou, G. and Li, Q. (2015). Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems. The Journal of Navigation, 68, 355366.Google Scholar
Georgy, J., Noureldin, A., Korenberg, M.J. and Bayoumi, M.M. (2010). Low-cost three-dimensional navigation solution for RISS/GPS integration using mixture particle filter. IEEE Transactions on Vehicular Technology, 59, 599615.Google Scholar
Grenon, G., An, P.E., Smith, S.M. and Healy, A.J. (2001). Enhancement of the Inertial Navigation System for the Morpheus Autonomous Underwater Vehicles. IEEE Journal of Oceanic Engineering, 26, 548560.Google Scholar
Grewal, M.S., Weill, L.R. and Andrews, A.P. (2007). Global Positioning System, Inertial Navigation, and Integration. John Wiley and Sons, Inc.Google Scholar
Hegrenaes, O. and Hallingstad, O. (2011). Model-aided INS with Sea Current Estimation for Robust Underwater Navigation. IEEE Journal of Oceanic Engineering, 36, 316337.Google Scholar
Hoshizaki, T. and Tashiro, E. (2009). Computational Scheme for MEMS Inertial Navigation Systems. Patent Application Publication.Google Scholar
Iqbal, U., Karamat, T.B., Okou, A.F. and Noureldin, A. (2009). Experimental results on an integrated GPS and multisensor system for land vehicle positioning. International Journal of Navigation and Observation, 2009, 118.Google Scholar
Iqbal, U., Okou, A.F. and Noureldin, A. (2008). An Integrated Reduced Inertial Sensor System-RISS / GPS for Land Vehicle. IEEE Proceedings of IEEE/ION PLANS, Monterey, CA.Google Scholar
Kaygisiz, B.H. and Sen, B. (2015). In-motion Alignment of a Low-cost GPS/INS under Large Heading Error. The Journal of Navigation, 68, 355366.Google Scholar
Kinsey, J.C., Eustice, R.M. and Whitcomb, L.L. (2006). A survey of Underwater Vehicle Navigation: Recent Advances and New Challenges. IFAC Conference Manoeuvre Control Marine Craft, Lisbon, Portugal.Google Scholar
Lee, P.M., Jun, B.H., Kim, K., Lee, J., Aoki, T. and Hyakudome, T. (2007). Simulation of an Inertial Acoustic Navigation System with Range Aiding for an Autonomous Underwater Vehicle. IEEE Journal of Oceanic Engineering, 32, 327345.Google Scholar
Maybeck, P.S. (1979). Stochastic Models, Estimation, and Control. Academic Press.Google Scholar
McEwen, R., Thomas, H., Weber, D. and Psota, F. (2005). Performance of an AUV Navigation System at Arctic Latitudes. IEEE Journal of Oceanic Engineering, 30, 443454.Google Scholar
Miller, P.A., Farrell, J.A., Zhao, Y. and Djapic, V. (2010). Autonomous Underwater Vehicle Navigation. IEEE Journal of Oceanic Engineering, 35, 663678.Google Scholar
Sarkka, S. (2013). Bayesian Filtering and Smoothing. Cambridge University Press.Google Scholar
Shabani, M. and Gholami, A. (2016). Improved Underwater Integrated Navigation System using Unscented Filtering Approach. The Journal of Navigation, 69, 561581.Google Scholar
Shabani, M., Gholami, A. and Davari, N. (2015). Asynchronous Direct Kalman Filtering Approach for Underwater Integrated Navigation System. Nonlinear Dynamics, 80, 7185.Google Scholar
Shabani, M., Gholami, A., Davari, N. and Emami, M. (2013). Implementation and Performance Comparison of Indirect Kalman Filtering Approaches for AUV Integrated Navigation System using Low Cost IMU. ICEE2013, Mashhad, Iran, 16.Google Scholar
Simon, D. (2006). Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches. John Wiley & Sons.Google Scholar
Titterton, D.H. and Weston, J.L. (2004). Strapdown Inertial Navigation Technology. The Institution of Electrical Engineers, Inc.Google Scholar
Valade, A., Acco, P., Grabolosa, P. and Fourniols, J.Y., (2017). A Study about Kalman Filters Applied to Embedded Sensors. MDPI Sensors, 17, 28102822.Google Scholar
Vasilijevic, A., Borovic, B. and Vukic, Z. (2012). Underwater Vehicle Localization with Complementary Filter: Performance Analysis in the Shallow Water Environment. Journal of Intelligent and Robotic System, 68, 373386.Google Scholar
Wang, Q., Li, Y. and Niu, X. (2016). Thermal Calibration Procedure and Thermal Characterization of Low-cost Inertial Measurement Units. The Journal of Navigation, 69, 373390.Google Scholar
Xian, Z., Hu, X. and Lian, J. (2014). Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach. The Journal of Navigation, 68, 737–452.Google Scholar
Yun, X., Bachmann, E.R., McGhee, R.B., Whalen, R.H., Roberts, R.L., Knapp, R.G., Healey, A.J. and Zyda, M.J. (1999). Testing and Evaluation of an Integrated GPS/INS System for Small AUV Navigation. IEEE Journal of Oceanic Engineering, 24, 396404.Google Scholar
Zhang, Q., Niu, X., Zhang, H. and Shi, C. (2013). Algorithm Improvement of the Low-end GNSS/INS Systems for Land Vehicles Navigation. Mathematical Problems in Engineering, 2013, 112.Google Scholar