Optimized Data Fusion in an Intelligent Integrated GPS/INS System Using Genetic Algorithm

Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with respect to the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS integration in comparison with conventional ANFIS specially in the cases of satellites- outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

Authors:

• Ali Asadian
• Behzad Moshiri
• Ali Khaki Sedigh
• Caro Lucas

Keywords:

• Adaptive Network based Fuzzy Inference System (ANFIS)
• Genetic optimization
• Global Positioning System (GPS)
• Inertial Navigation System (INS).

References:

[1] D. J. C. MacKay, Information theory, inference and learning algorithms,
U.K., Cambridge Univ. Press, 2003.
[2] J. A. Farrel and M. Barth, The global positioning system and inertial
navigation, New York: McGraw-Hill, 1999.
[3] J. Shing and R. Shang, "ANFIS : adaptive-network-based fuzzy
inference system," IEEE Trans. Systems, Man and Cybernetics, vol. 23,
no. 3, pp. 665-685, May 1993.
[4] P. D. Stroud, "Kalman-Extended genetic algorithm for search in
nonstationary environments with noisy fitness evaluations," IEEE Trans.
Evolutionary Computation, vol. 5, no. 1, pp. 66-77, February 2001.
[5] T. Geisler and T.W. Manikas, "Autonomous robot navigation system
using a novel value encoded genetic algorithm," In Proc. 45th IEEE Int.
Midwest Symp. on Circuits and Systems, 2002, p. 45-48.
[6] D. Loebis, R. Sutton and J. Chudley, "A fuzzy kalman filter optimized
using a genetic algorithm for accurate navigation of an autonomous
underwater vehicle," In Proc. MCMC2003 Conf., September 17-19,
Girona, Spain, pp. 19-24.
[7] S. J. Simske, "Navigation using hybrid genetic programming : initial
conditions and state transitions," HP intelligent enterprise Tech. Lab.,
Tech. Rep., Palo Alto, March 2003.
[8] S. Luke and L. Spector, "A revised comparison of crossover and
mutation in genetic programming," In Proc. 3th Annu. Genetic
Programming Conf. (GP98). San Fransisco: Morgan Kaufmann, 1998,
pp. 208-213.
[9] A. Hiliuta, R. Jr. Landry and F. Gagnon, "Fuzzy correction in a GPS/INS
hybrid navigation system," IEEE Trans. Aerospace and Electronic
Systems, vol. 40, no. 2, pp. 591-600, April 2004.
[10] J. H. Wang and Y. Gao, "Evaluating the accuracy of GPS positions
under severe signal-degradation using adaptive-network-based fuzzy
inference systems (ANFIS)," presented at the 50th CASI Annu. General
meeting and Conf., Canadian Aeronautics and Space Inst., Montréal,
Quebec, Canada, April 28 - 30, 2003.
[11] D. Mc. N. Mayhew, "Multi-rate sensor fusion for GPS navigation using
kalman filtering," M.S. thesis, Dept. Elect. Eng., Virginia Polytech. Inst.
and State Univ., Virginia, USA, 1999.
[12] X. He, Y. Chen and H.B.Iz, "A reduced-order model for integrated
GPS/INS," IEEE AES Systems Magazine, pp. 40-45, MARCH 1998.
[13] B. Azimi-Sadjadi, "Approximate nonlinear filtering with applications to
navigation," Ph.D. dissertation, Dept. Elect. Eng., Maryland Univ.,
College Park, 2001.