An Intelligent GPS/Ins Integrated System Using Co-Active Neuro-Fuzzy Inference System (CANFIS)

Elkhidir Tay-allah Yousif, Abd-elraheem M. H. Satti

Abstract


The Global Positioning System (GPS) and Inertial Navigation System (INS) have complementary operational characteristics. To have the benefits of these systems and to overcome their disadvantages, GPS and INS are integrated together to provide accurate position, velocity, and attitude. In this paper, a novel technique based on Co-active Neuro-Fuzzy Inference System (CANFIS) is proposed to implement GPS/MEMS-INS integration. The proposed CANFIS is constructed of the neural network adaptive capabilities and the fuzzy logic qualitative approach. The performance of the CANFIS is examined. In fact, this is extremely difficult to achieve if the Micro-Electro-Mechanical-Systems (MEMS) based inertial sensors are involved in the integrated INS/GPS system due to their relatively high measurement noise. CANFIS is used to model the operation of the MEMS sensors so as to reduce the integrated system’s errors. Experimental results were reached and are outlined in this paper. The results of the study are highly promising and suggest that CANFIS modeling is a more flexible and enhanced alternative to the corresponding conventional models approach in combating MEMS errors.

Keywords


CANFIS; GPS; GPS/INS integration; MEMS-INS

Full Text:

PDF

References


Aboelmagd Noureldin, Tashfeen B. Karamat, Mark D. Eberts, and Ahmed El-Shafie,” Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications,” IEEE Transactions On Vehicular Technology, Vol. 58, pp. 1077-1096, No. 3, March 2009. [2] Ahmed M. Hasan, Khairulmizam Samsudin, Abd Rahman Ramli, Raja Syamsul Azmir, and Salam Ismaeel “A Review of Navigation Systems (integration and algorithms),” Australian Journal of Basic and Applied Sciences, 3(2): pp. 943-959, 2009 ISSN 1991-8178 © 2009, INSInet Publication. [3] Ahmed El_Shafie, Aini Hussain, and Aboelmagd Noureldin, “ANFIS-Based Model for Real-time INS/GPS Data Fusion for Vehicular Navigation System,” IEEE 2009 International Conference on Computer Technology and Development, pp. 278-282 [4] Ali Aytek 2008 “Co-active neuro-fuzzy inference system for evapotranspiration modelling” © Springer-Verlag, soft computing, pp. 691-700, 2008. [5] Kai-Wei Chiang, Aboelmagd Noureldin, Naser El-Sheimy, “Constructive Neural-Networks-Based MEMS/GPS Integration Scheme,” IEEE Transactions On Aerospace And Electronic Systems Vol. 44, pp. 582-594, No. 2 April 2008. [6] Jang, and Jyh-Shing Roger, “Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence,” © 1997; Upper Saddle River, N.J. :Prentice Hall. ISBN: 0132610663. [7] M. Malleswaran, J.Mary Anita, S.N.Sabreen and V. Vaidehi, “Real-time INS/GPS Data Fusion Using Hybrid Adaptive Network Based Fuzzy Inference,” IEEE 11th Int. Conf. Control, Automation, Robotics and Vision, Singapore, pp. 1536-1540, 7-10th December 2010.


Refbacks

  • There are currently no refbacks.


Sudan Eng. Society Journals