An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration

Wassim Khoder; Bassem Jida

doi:doi:10.11648/j.cssp.20140302.11

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An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration

Wassim Khoder, Bassem Jida

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 3, Issue 2)

Received: 27 August 2014 Accepted: 22 September 2014 Published: 30 September 2014

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Abstract

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.

Published in	Science Journal of Circuits, Systems and Signal Processing (Volume 3, Issue 2)
DOI	10.11648/j.cssp.20140302.11
Page(s)	5-13
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Inertial Navigation System, GPS, Magnetometer, Takagi-Sugeno Fuzzy Model, Fuzzy C-Means, Mean Square Error

References

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[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	J. Bezdek. Pattern Recognition with Fuzzy Objective Function, Plenum Press, New York, 1981.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	K. J. Astrom and B. Wittenmark. Computer Controller Systems: Theory and Design, Prentice-Hall, Inc., 1984.
[14]	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.

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APA Style

Wassim Khoder, Bassem Jida. (2014). 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, 3(2), 5-13. https://doi.org/10.11648/j.cssp.20140302.11

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ACS Style

Wassim Khoder; Bassem Jida. An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration. Sci. J. Circuits Syst. Signal Process. 2014, 3(2), 5-13. doi: 10.11648/j.cssp.20140302.11

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AMA Style

Wassim Khoder, Bassem Jida. An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration. Sci J Circuits Syst Signal Process. 2014;3(2):5-13. doi: 10.11648/j.cssp.20140302.11

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@article{10.11648/j.cssp.20140302.11,
  author = {Wassim Khoder and Bassem Jida},
  title = {An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {3},
  number = {2},
  pages = {5-13},
  doi = {10.11648/j.cssp.20140302.11},
  url = {https://doi.org/10.11648/j.cssp.20140302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20140302.11},
  abstract = {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.},
 year = {2014}
}

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TY  - JOUR
T1  - An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration
AU  - Wassim Khoder
AU  - Bassem Jida
Y1  - 2014/09/30
PY  - 2014
N1  - https://doi.org/10.11648/j.cssp.20140302.11
DO  - 10.11648/j.cssp.20140302.11
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 5
EP  - 13
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20140302.11
AB  - 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.
VL  - 3
IS  - 2
ER  -

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Author Information

Wassim Khoder

Faculty of Economics and Business Administration, Lebanese University, Tripoli, Lebanon
Bassem Jida

Faculty of Letters, Lebanese University, Tripoli, Lebanon

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APA Style

Wassim Khoder, Bassem Jida. (2014). 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, 3(2), 5-13. https://doi.org/10.11648/j.cssp.20140302.11

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ACS Style

Wassim Khoder; Bassem Jida. An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration. Sci. J. Circuits Syst. Signal Process. 2014, 3(2), 5-13. doi: 10.11648/j.cssp.20140302.11

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AMA Style

Wassim Khoder, Bassem Jida. An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration. Sci J Circuits Syst Signal Process. 2014;3(2):5-13. doi: 10.11648/j.cssp.20140302.11

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@article{10.11648/j.cssp.20140302.11,
  author = {Wassim Khoder and Bassem Jida},
  title = {An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {3},
  number = {2},
  pages = {5-13},
  doi = {10.11648/j.cssp.20140302.11},
  url = {https://doi.org/10.11648/j.cssp.20140302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20140302.11},
  abstract = {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.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - An Adaptive Fuzzy Logic Quaternion Scaled Unscented Kalman Filtering for Inertial Navigation System, GPS and Magnetometer Sensors Integration
AU  - Wassim Khoder
AU  - Bassem Jida
Y1  - 2014/09/30
PY  - 2014
N1  - https://doi.org/10.11648/j.cssp.20140302.11
DO  - 10.11648/j.cssp.20140302.11
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 5
EP  - 13
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20140302.11
AB  - 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.
VL  - 3
IS  - 2
ER  -

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