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Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation


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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  • Brown RG, Hwang PYC (1992) Introduction to random signals. Wiley, New York

    MATH  Google Scholar 

  • Bshouty NH, Burroughs L (2005) Maximizing agreements with one-sided error with applications to Heuristic learning. Mach Learn 59 (1–2):99–123

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    MATH  Google Scholar 

  • Laviolette M, Seaman JW, Barrett JD, Woodall WH (1995) A probabilistic and statistical view of fuzzy methods. Technometrics 37:249–261

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Passino K, Yurkovich S (1998) Fuzzy control. Addison Wesley, CA

    Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Vanicek P, Omerbasic M (1999) Does a navigation algorithm have to use Kalman filter?. Can Aeronaut Space J 45 (3)

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Correspondence to Mahmoud Reda Taha.

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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).

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  • Fuzzy systems
  • Inertial navigation
  • Data fusion
  • Positioning systems