Given the fact that the incorporation of artifi cial intelligence to the navigation algorithm is new to the navigation community, it needs more extensive research to accelerate wider inclusion of such an idea to commercial products. In fact, using artifi cial intelligence for mobile robot navigation has been studied extensively in robotic engineering related research works since the fi eld of artifi cial intelligence started. Therefore, developing a new artifi cial intelligent INS/GPS integration architecture that can overcome some of the limitations of the traditional navigator in a land vehicle environment is a huge challenge. However, the results presented in this article strongly indicate the potential of including the artifi cial intelligence as the core navigation algorithm for the next generation land vehicular navigation system.

This article exploited the incorporation of artifi cial neural networks to develop an alternative INS/DGPS integration scheme, the conceptual intelligent navigator, for low cost MEMS IMU/ DGPS integrated land vehicular navigation system. The preliminarily results presented in this article reached the goal which was set to reduce the positional errors, generated by the limiting factors of traditional navigator, during GPS signal outages.

The conceptual intelligent navigator was able to improve the positioning accuracy during GPS signal outages applied in both scenarios. The overall improvement reached 47% and 78, respectively. The results presented in this article illustrated that the positioning accuracy of traditional navigator decreased with longer GPS signal outage period. In contrast, the positioning accuracy of the conceptual intelligent became more signifi cant with longer GPS signal outage periods.

Given the fact that the incorporation of artifi cial intelligence to the navigation algorithm is new to the navigation community, it needs more extensive research to accelerate wider inclusion of such an idea to commercial products. However, the results presented in this article strongly indicate the potential of including the artifi cial intelligence as the core navigation algorithm for the next generation land vehicular navigation system.

The authors would like to thank the fi nancial support by Geoide NCE and NSERC funds. Eun- Hwan Shin is acknowledged for providing the INS toolbox applied in this article to provide the outputs of INS mechanization and extended Kalman fi lter

Cawsey, A. (1998): The Essence of Artifi cial Intelligence, Prentice Hall PTR.

Chiang, K.W. and El-Sheimy, N. (2002): INS/GPS Integration using Neural Networks for Land Vehicle Navigation Applications, Proceedings of the US Institute of Navigation (ION) GPS’2002 meeting, September 24-27, 2002 - Oregon Convention Center, Portland, Oregon, USA (CD).

Chiang, K.W., Noureldin, A., and El-Sheimy, N.(2003): Multi-sensors Integration using Neuron Computing for Land Vehicle Navigation, GPS Solutions.,Vol. 6, No. 3, pp. 209-218.

Chiang, K.W.(2004): Development of an Optimal GPS/MEMS Integration Architecture for Land Vehicle Navigation Utilizing Neural Network. Journal of Global Position System and CPGPS student paper competition (Best Student Paper Award).

Chiang, K.W., El-Sheimy, N., and Noureldin, A. (2004): A New Weights Updating Method for Neural Networks based INS/GPS Integration Architectures, Measurement Science and Technology, Vol. 15, No.10, pp. 2053-2061.

Honavar, V. and Uhr L. (1994): Artifi cial Intelligence and Neural Networks: Steps Toward Principled Integration, Boston: Academic Press.