DVL/RPM Based Velocity Filter Aiding in the Underwater Vehicle Integrated Inertial Navigation System

DOI: 10.4236/jst.2014.43015   PDF   HTML     4,114 Downloads   4,810 Views   Citations


The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The integrated navigation sensor using DVL (Doppler Velocity Log) is widely used to improve the underwater navigation performance. However, the DVL’s range of measuring varied depending on the characteristics of sensor. So, if the sea gets too deep suddenly, it cannot measure the velocity. To complement such a weak point, the VKF was additionally designed, which was made of DVL, RPM (Revolve Per Minutes) of motor, and ES (Echo Sounder). The proposed approach relies on a VKF, augmented by an altitude from ES based switching architecture to yield robust performance, even when DVL exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate the proposed method, we compare the VKF aided navigation system with PINS (Pure Inertial Navigation System) and conventional INS-DVL navigation system through simulation results. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.

Share and Cite:

Yoo, T. (2014) DVL/RPM Based Velocity Filter Aiding in the Underwater Vehicle Integrated Inertial Navigation System. Journal of Sensor Technology, 4, 154-164. doi: 10.4236/jst.2014.43015.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Titterton, D.H. and Weston, J.L. (1997) Strapdown Inertial Navigation Technology. Peter Pegerinus, London.
[2] Yoo, T.S. and Hong, S.K. (2011) Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System. Sensors, 11, 3816-3830.
[3] Lee, J.M. and Lee, P.M. (2003) Underwater Hybrid Navigation Algorithm Based on an Inertial Sensor and a Doppler Velocity Log Using an Indirect Feedback Kalman Filter. Journal of Ocean Engineering and Technology, 17, 83-90.
[4] Lee, J.M., Lee, P.M., Kim, S.M., Hong, S.W., Seo, J.W. and Seong, W.J. (2003) Rotating Arm Test for Assessment of an Underwater Hybrid Navigation System for a Semi-Autonomous Underwater Vehicle. Journal of Ocean Engineering and Technology, 17, 73-80.
[5] Lee, P.M., Jeon, B.H., Kim, S.M., Lee, J.M., Lim, Y.K. and Yang, S.I. (2004) A Hybrid Navigation System for Underwater Unmanned Vehicles, Using a Range Sonar. Journal of Ocean Engineering and Technology, 18, 33-39.
[6] Li, W., Wang, J., Lu, L. and Wu, W. (2013) A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques. Sensors, 13, 1046-1063.
[7] Geng, Y., Martins, R. and Sousa, J. (2010) Accuracy Analysis of DVL/IMU/Magnetometer Integrated Navigation System Using Different IMUs in AUV. 8th IEEE ICCA, Xiamen, 516-521.
[8] Crassidis, J.L. and Cheng, Y. (2007) Nonlirear Attitude Filtering Methods. Journal of Guidance, Control, and Dynamics, 30, 12-28.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.