An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration
Science Journal of Circuits, Systems and Signal Processing
Volume 3, Issue 2, April 2014, Pages: 5-13
Received: Aug. 27, 2014;
Accepted: Sep. 22, 2014;
Published: Sep. 30, 2014
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Wassim Khoder, Faculty of Economics and Business Administration, Lebanese University, Tripoli, Lebanon
Bassem Jida, Faculty of Letters, Lebanese University, Tripoli, Lebanon
In this paper, we present a technique based on fuzzy logic to improve the performance of the inertial navigation system integrated with GPS, and magnetometer. The proposed fuzzy technique is primarily used to predict position and velocity measurements during GPS outage signals. As long as the GPS measurements are available, the Q-SUKF of INS/GPS/MAG (MAG: magnetometer) integrated system operates efficiently and provides precise navigation states estimation. Nevertheless, during GPS outage signals, the proposed fuzzy technique is adapted to the Q-SUKF to obtain the (A) (FL) QSUKF (Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filter) in order to correct the degradation of the performance of the algorithm. The adaptive fuzzy logic attributes values to the measurements covariance matrix in order to determine the gain of the filter. It will decrease the measurement noise variance of the Kalman filter and then improves eventually the accuracy of the integrated navigation system states estimation. Finally, an experimental part on the use of the proposed fuzzy technical with the Q-SUKF has been validated. Several GPS outages with duration of 30s have been simulated to study the behavior of the proposed filter. In addition, an initial attitude error of 60 degrees is given in each axis to test the robustness of the filter proposed under large attitude errors. The results of the experimental validation have shown the effectiveness and the significant impact of the (A) (FL) Q-SUKF in the reduction of the drift errors estimation of the position and velocity in case of GPS outages in the tested scenarios.
An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration, Science Journal of Circuits, Systems and Signal Processing.
Vol. 3, No. 2,
2014, pp. 5-13.
W. Khoder and B. Jida. A Quaternion Scaled Unscented Kalman Estimator for Inertial Navi- gation States Determination Using INS/GPS/Magnetometer Fusion, Journal of Sensor Technology, vol. 4, no. 2, pp. 101-117, June 2014. http://dx.doi.org/10.4236/jst.2014.42010
R. Babuska, JA Roubos, and H.B. Verbruggen. Identification of MIMO systems by input-output TS fuzzy models, In: The 1998 IEEE international conference on fuzzy systems, vol 1, pp. 657-662, Anchorage, Alaska, 1998.
H. Wang, K. Tanaka and M. Griffin. An approach to fuzzy control of nonlinear systems: stability and design issues, IEEE transactions on fuzzy systems, Vol. 4, pp. 14-23, 1996.
Naderi, M. Aliasghary, A. Pourazar and H. Ghasemzadeh: “A 19MFLIPS CMOS Fuzzy Controller to Control Continuously Variable Transmission Ratio”, Ph.D. Research in Microelectronics and Electronics (PRIME), 2011 7th Conference on , vol., no., pp.45,48, 3-7 July 2011.
A. Naderi and S. Ozoguz, “Programmable Implementation of Diamond-Shaped Type-2 Membership Function in CMOS Technology”, Journal of Circuit, System and Signal Processing, 2014, doi: 10.1007/s00034-014-9846-x.
J. Abonyi, JA. Roubos, M. Oosterom, and F. Szeifert. Compact TS-Fuzzy Models through Clustering and OLS plus FIS Model Reduction, In : The 10th IEEE international conference on fuzzy systems, vol. 3, pp. 1420-1423, 2001.
J. Dunn. A fuzzy relative to ISODATA process and its use in detecting compact well separated clusters, Journal of Cybernetics, vol.3, n3, pp. 32-57, 1974.
J. Bezdek. Pattern Recognition with Fuzzy Objective Function, Plenum Press, New York, 1981.
V.-H. Grisales Palacio. MODÉLISATION ET COMMANDE FLOUES DE TYPE TAKAGI-SUGENO APPLIQUÉES À UN BIOPROCÉDÉ DE TRAITEMENT DES EAUX USÉES, Ph.D. dissertation, Paul Sabatier University, Toulouse III, 2007.
S.A. Billings, M.J. Korenberg, and S. Chen. Identification of nonlinear output-affine systems using an orthogonal least-squares algorithm, Int J Sys Sci, vol. 19, pp.1559-1568, 1998.
N. Pal and J. Bezdek. On cluster validity for the fuzzy c-means model, IEEE Transactions on Fuzzy Systems, vol. no 3, pp. 370-379, 1995.
J. Yu, Q. Cheng and H. Huang. Analysis of the weighting exponent in the FCM, IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, vol.34, no.1, pp. 634-639, 2004.
K. J. Astrom and B. Wittenmark. Computer Controller Systems: Theory and Design, Prentice-Hall, Inc., 1984.
S. E Fahlman. Faster-learning variations on back-propagation: an optimal study, Proceedings of the 1988 Connectionist Models Summer School, pp. 38-51, Carnegic Model University, 1988.