Sage Journals: Discover world-class research

Abstract

To overcome the disadvantages of the conventional federated Kalman filter, a fuzzy Kalman filter based on a genetic algorithm (GA) is presented in this paper and applied in the information fusion of the Global Positioning System—dead reckoning vehicle integrated navigation system. The noise covariance and information distribution coefficient of the local filtering are modified online by the fuzzy logic adaptive controller in order to make the Kalman filtering optimal and to improve the positioning accuracy of the integrated navigation system. The acquisition of the membership function of a fuzzy controller usually relies to a great extent on empirical and heuristic knowledge. In this paper, the GA is used to optimize the fuzzy membership function and obtain the optimal or suboptimal control effect. The results of simulations demonstrate the feasibility and effectiveness of the method.

Keywords

vehicle navigation system federated Kalman filter fuzzy control genetic algorithm membership function

Get full access to this article

View all access options for this article.

References

Dedes

Nonlinear smoothing of dead reckoning data with GPS measurements. In Proceedings of the Eighth International Technical Meeting of the Institute of Navigation (ION GPS-95), California, 12–15 September, 1995, pp. 1285–1294.

Zhang

S.-T.

Land vehicle location orientation navigation system and multi-sensor integration (in Chinese). PhD Dissertation, Beijing Institute of Technology, Beijing, People's Republic of China, 1998.

Vlcek

Mclain

Murphy

GPS/dead reckoning for vehicle tracking in the urban canyon environment. In Proceedings of the IEEE-IEE Conference on

Vehicle navigation and information systems, 12–15 October, 1993, pp. A36–A37 (IEEE, IEE, New York).

Longo

Muscato

Sacco

Localization using fuzzy and Kalman filtering data fusion. In Proceedings of the Fifth International Conference on

Climbing and walking robots and their supporting technologies (CLAWAR 2002), Paris, France, 25–27 September, 2002 (Professional Engineering Publishing Limited, Bury St Edmunds).

Escamilla-Ambrosio

P. J.

Mort

Multi-sensor data fusion architecture based on adaptive Kalman filters and fuzzy logic performance assessment. In Proceedings of the 2002 First IEEE International Symposium on

Information fusion, 10–12 July, 2002, Vol. 1, pp. 1542–1549 (IEEE, New York).

Escamilla-Ambrosio

P. J.

Mort

A hybrid Kalman filter-fuzzy logic architecture for multi-sensor data fusion. In Proceedings of the 2001 IEEE International Symposium on

Intelligent control, 5–7 September, 2001, pp. 364–369 (IEEE, New York).

Green

G. B.

Massatt

P. D.

Rhodus

N. W.

The GPS 21 primary satellite constellation. Navigation, 1989, 36 (1), 9–24.

Grewal

M. S.

Andrew

A. P.

Kalman filtering: Theory and practice, 1993 (Prentice-Hall, Englewood Cliffs, New Jersey).

Carlson

H. A.

Federated filter for fault-tolerant integrated navigation systems. In Proceedings of the IEEE Symposium on

Position location and navigation symposium (PLANS), 29 November-2 December, 1998, pp. 110–119 (IEEE, New York).

10.

Carlson

H. A.

Information-sharing approach to federated Kalman filtering. In Proceedings of the IEEE National Aerospace and Electronics Conference, 1998, 1581 (IEEE, New York).

11.

Park

Kandel

Langholz

Genetic-based new fuzzy reasoning models with application to fuzzy control. IEEE Trans. Systems, Man, Cybernetics, 1994, 24 (1), 39–47.

12.

Wang

L. X.

Mendel

J. M.

Generating fuzzy rules by learning from example. IEEE Trans. Systems, Man, Cybernetics, 1992, 22 (6), 1414–1427.

Research on the information fusion method of the Global Positioning System-dead reckoning vehicle integrated navigation system

Abstract

Abstract

Keywords

Get full access to this article

References