Abstract
Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However, KF-based integration techniques suffer from several limitations related to its immunity to noise, observability and the necessity of accurate stochastic models of sensor random errors. This article investigates the potential use of adaptive neuro-fuzzy inference system (ANFIS) for temporal integration of INS/GPS in vehicular navigation. An ANFIS-based module named “P–δP” is designed, developed, implemented and tested for fusing INS and GPS position information. The fusion process aims at providing continuous correction of INS position to prevent its long-term growth using GPS position updates. In addition, it provides reliable prediction of the vehicle position during GPS outages. The P–δP module was examined using real navigation system data compromising an Ashtech Z12 GPS receiver and a Honeywell LRF-III INS. The proposed module proved to be successful as a modeless and platform independent module that does not require a priori knowledge of the navigation equipment utilized. Limitations of the ANFIS module are also discussed.
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References
Brown RG, Hwang PYC (1992) Introduction to random signals. Wiley, New York
Bshouty NH, Burroughs L (2005) Maximizing agreements with one-sided error with applications to Heuristic learning. Mach Learn 59 (1–2):99–123
Chiang K-W, Noureldin A, El-Sheimy N (2006) The utilization of artificial neural networks for multi-sensor system integration in navigation and positioning instruments: IEEE Trans Instrum Measure 55(5).
Deb K, Gupta H (2005) Searching for robust pareto-optimal solutions in multi-objective Optimization. In: Proceedings of third international conference on evolutionary multi-criterion optimization CAC Coello, AH, Aguirre, E, Zitzler (eds), Guanajuato, Mexico, Lecture Notes in Computer Science 3410, Springer, Berlin Heidelberg New York
El-Sheimy N (1996) The development of VISAT - a mobile survey system for GIS applications. Ph.D. thesis, Department of Geomatics Engineering, The University of Calgary, UCGE Report No. 20101
El-Sheimy N, Schwarz KP (1994) Integrating differential GPS receivers with an inertial navigation system (INS) and CCD cameras for a mobile GIS data collection system. In: ISPRS94, Ottawa, Canada, pp 241–248
Farrell J (1998) The global positioning system and inertial navigation. McGraw-Hill, New York
Hargrave PJ (1989) A tutorial introduction to Kalman filtering. IEE colloquium on Kalman filters: introduction, applications and future developments, pp 1–6
Hostetler L, Andreas R (1983) Nonlinear Kalman filtering techniques for terrain-aided navigation. IEEE Trans Automatic Control 28(3): 315–323
IEEE standard 647(1995) IEEE standard specification formal guide and test procedure for single-axis laser gyros
Lobo J, Lucas P, Dias J, Traca de Almeida A (1995) Inertial navigation system for mobile land vehicles. In: Proceedings IEEE international symposion on industrial electronics ISIE ’95, Vol. 2, pp 843–848
Jang JSR (1993) ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23(3):665–685
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence. Prentice Hall, Englewood Cliffs
Klir G, Yaun B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, Upper Saddle River
Laviolette M, Seaman JW, Barrett JD, Woodall WH (1995) A probabilistic and statistical view of fuzzy methods. Technometrics 37:249–261
Mynbaev DK (1994) Errors of an inertial navigation unit caused by ring laser gyros errors In: IEEE position location and navigation symposium, pp 833–838
Nassar S, Noureldin A, El-Sheimy N (2004) Improving positioning accuracy during kinematic DGPS outage periods using SINS/DGPS integration and SINS data de-noising. Surv Rev 37(292):426–438
Noureldin A, El-Shafie A, Reda Taha M (2006) Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. In: Engineering Applications of Artificial Intelligence, Vol.19(7). Elsevier, Amsterdam.
Noureldin A, Osman A, El-Sheimy N (2004) A neuro-wavelet method for multi-sensor system integration for vehicular navigation. J Measure Sci Technol 15(2):404–412
Noureldin A, Irvine-Halliday D, Mintchev MP (2002) Accuracy limitations of FOG-based continuous measurement-while-drilling surveying instruments for horizontal wells. IEEE Trans Inst Measure 51(6): 1177–1191
Passino K, Yurkovich S (1998) Fuzzy control. Addison Wesley, CA
Ross TJ (2004) Fuzzy logic with engineering applications. Wiley West Sussex
Salychev O (1998) Inertial systems in navigation and geophysics Bauman MSTU Press, Moscow
Scherzinger BM, Reid DB (1994) Modified strapdown inertial navigator error models. In: IEEE Position location and navigation Symposium, pp 426–430
Singpurwalla ND, Booker JM (2004) Membership functions and probability measures of fuzzy sets. J Ame Stat Assoc 99 (467): 867–877
Vanicek P, Omerbasic M (1999) Does a navigation algorithm have to use Kalman filter?. Can Aeronaut Space J 45 (3)
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Sharaf, R., Taha, M.R., Tarbouchi, M. et al. Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation. Soft Comput 11, 889–900 (2007). https://doi.org/10.1007/s00500-006-0140-0
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DOI: https://doi.org/10.1007/s00500-006-0140-0
Keywords
- Fuzzy systems
- Inertial navigation
- Data fusion
- Positioning systems