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IMM-UKF-TFS Model-based Approach for Intelligent Navigation

Published online by Cambridge University Press:  19 July 2013

M. Malleswaran ,
V. Vaidehi ,
S. Irwin  and
B. Robin
M. Malleswaran*
Affiliation:
(Department of Electronics and Communication Engineering, Regional Centre of Anna University, Tirunelveli Region, Tamilnadu, India)
V. Vaidehi
Affiliation:
(Department of Electronics and Communication Engineering, Regional Centre of Anna University, Tirunelveli Region, Tamilnadu, India)
S. Irwin
Affiliation:
(Department of Electronics and Communication Engineering, Regional Centre of Anna University, Tirunelveli Region, Tamilnadu, India)
B. Robin
Affiliation:
(Department of Electronics and Communication Engineering, Regional Centre of Anna University, Tirunelveli Region, Tamilnadu, India)
*
(Email: [email protected])
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Abstract

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.

Keywords

GPSINSNavigationIMM-UKF-TFS
Type
Research Article
Information
The Journal of Navigation , Volume 66 , Issue 6 , November 2013 , pp. 859 - 877
Copyright
Copyright © The Royal Institute of Navigation 2013 

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References

REFERENCES

Almagbile, A. and Wang, J. (2010). Evaluating the performances of Kalman Filter Methods in GPS /INS Integration. Journal of Global positioning Systems, 9, 33–40.CrossRefGoogle Scholar
Bar-Shalom, Y.B., Rong Li, X. and Kirubarajan, T. (2001). Estimations with applications to tracking and navigation. Wiley Interscience Publications.Google Scholar
Fraser, D.C. and Potter, J.E. (1969). The optimum linear smoother as a combination of two optimum linear filters. IEEE Transactions on automatic control, AC-14, 387–390.CrossRefGoogle Scholar
Grewal, M. S., Weill, L.R. and Andrews, A.P. (2001). Global Positioning Systems Inertial Navigation and Integration. Wiley Interscience Publications.Google Scholar
Grewal, M.S. and Andrews, A.P. (2008). Kalman Filtering Theory and Practice using Matlab, 3rd edition. John & Wiley Publications.CrossRefGoogle Scholar
Haykin, S. (2001). Kalman Filtering and Neural Networks, Wiley Interscience Publications.CrossRefGoogle Scholar
Helmik, R.E., Blair, W.D. and Hoffman, S.A.(1996). One-Step Fixed Lag Smoothers for Markovian Switching Systems. IEEE Transactions on Automatic Control. 41(7), 1051–1056.CrossRefGoogle Scholar
Hide, C., Moore, T. and Smith, M. (2003). Adaptive Kalman Filtering for Low-cost INS/GPS. The Journal of Navigation, 56, 143–152.CrossRefGoogle Scholar
Honghui, Qi (2002). Direct Kalman Filtering approach for GPS-INS Integration. IEEE Transaction on Aerospace and Electronic systems. 37, 687–693.CrossRefGoogle Scholar
Kim, Y. and Hong, K. (2004). An IMM algorithm for tracking maneuvering vehicles in an adaptive cruise control environment. International Journal of Control, Automation and Systems. 2, 310–318.Google Scholar
Liu, H., Nassar, S. and El-Sheimy, N. (2010). Two Filter Smoothing for Accurate INS/GPS Land Vehicle Navigation in Urban Centers. IEEE Transactions on Vehicular Technology. 59, 4256–4267.CrossRefGoogle Scholar
Qian, H., An, D. and Xia, Q. (2010). IMM-UKF based land vehicle naviagion with low-cost GPS-INS. Proceedings of the 2010 IEEE International Conferene on Information and Automation, Harbin, China, 2031–2035.CrossRefGoogle Scholar
Rong Li, X. and Bar-Shalom, Y. (1993a). Design of an Interacting Multiple Model Algorithm for Air Traffic Control Tracking. IEEE Transactions on Control Systems Technology. 1(3).Google Scholar
Rong Li, X. and Bar-Shalom, Y. (1993b). Performance prediction of the interacting multiple model algorithm. IEEE Transactions on Aerospace and Electronic Systems, 29(3), 755–771.Google Scholar
Rong Li, X. and Vesselin, P.J. (2003). Survey of Maneuvering Target Tracking. Part.I:Dynamic Models. IEEE Transaction on Aerospace and Electronic systems, 39, 1333–1364.CrossRefGoogle Scholar
Rauch, Tung and Striebel (1965). Maximum Likelihood Estimates of Linear Dynamic Systems. AIAA JOURNAL, 3(8).Google Scholar
Tseng, C.H. and Jwo, D.J. (2009). GPS navigation processing using the IMM based Extended Kalman Filter for integrated navigation sensor fusion. Sensor Publications, 6, 4–17.Google Scholar