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Multi-sensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms

Published online by Cambridge University Press:  04 July 2016

R. S. Doyle  and
C. J. Harris
R. S. Doyle
Affiliation:
Department of Electronics and Computer ScienceUniversity of SouthamptonSouthampton, UK
C. J. Harris
Affiliation:
Department of Electronics and Computer ScienceUniversity of SouthamptonSouthampton, UK
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Abstract

The purpose of this paper is to describe an approach which performs data fusion on the output of multiple, spatially separate, sensors engaged in the real time tracking of obstacles in a helicopter's environment. The generated information can be used either as a flight director aid or as feedback required by an automatic collision avoidance system. Obstacle track estimation has been commonly carried out using the Kalman filter (KF) for linear estimation, or the extended Kalman filter (EKF) for use on nonlinear problems. However, certain assumptions made in the derivation of the EKF algorithms render it sub-optimal for aerial obstacle track estimation. Additionally, the EKF has problems with initialisation and divergence (stability) for many non-linear processes.

Research at the University of Southampton has highlighted a link between fuzzy networks and associative memory neural networks. This link is important as it allows new learning rules to be developed for training fuzzy rules, and learning convergence to be proved. This paper explores methods of fusion of estimates using neuro-fuzzy models, and addresses some of the weakness of the Kalman filter approximation introduced by the assumptions made in its derivation.

Type
Research Article
Information
The Aeronautical Journal , Volume 100 , Issue 996 , July 1996 , pp. 241 - 251
Copyright
Copyright © Royal Aeronautical Society 1996 

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References

1. Bar-Shalom, Y. (ed) Multi-target, Multisensor Tracking: Advanced Applications, Artech House, 685 Canton Street, Norwood, MA 02062, 1989.Google Scholar
2. Waltz, E. and Llinas, J. Multisensor Data Fusion, Artech House, 685 Canton Street, Norwood, MA 02062, first edition, 1990.Google Scholar
3. Brown, M. and Harris, C.J. Neuro-fuzzy Adaptible Modelling and Control. Systems and Control Engineering, Prentice Hall, Hemel Hempstead, UK, 1994.Google Scholar
4. Harris, C.J., Moore, C.G. and Brown, M. Intelligent Control: Aspects of fuzzy logic and neural networks, World Scientific, 73 Lyn- ton Mead, Totteridge, London N20 8DH, 1993.Google Scholar
5. Gueziec, A. and Ayache, N. Smoothing and matching of 3-D space curves, Technical Report 1544, Inria, 1991.Google Scholar
6. Cox, M.G. The numerical evaluation of b-splines, J Inst Math Appl, 1972, 10, pp 134–149.Google Scholar
7. Deboor, C. On calculating with b-splines, J Approx Theory, 1972, 6, pp 50–62.Google Scholar
8. Borrie, J.A. Stochastic Systems for Engineers, Prentice Hall International (UK), 1992.Google Scholar
9. Bozic, S.M. Digital and Kalman Filtering, Edward Arnold, 41 Bedford Square, London WC1B 3DQ, 1979.Google Scholar
10. Chui, C.K. and Chen, G. Kalman Filtering with Real-Time Applications, Vol 17, Springer Series in Information Sciences, Springer- Verlag, Berlin Heidelberg, 1987.Google Scholar
11. Dodd, T.J. An Introduction to Kalman Filtering in Real-Time Tracking, Technical Report TM AIP055, British Aerospace (Operations), Sowerby Research Centre, Sowerby, UK, September 1993.Google Scholar
12. Doyle, R.S. and Harris, C.J. The Kalman Filter, Technical Report Helios/TN/5, University of Southampton, ASRG, Dept Aero and Astro, University of Southampton, Highfield, Southampton, UK, July 1994.Google Scholar
13. Durrant-Whyte, H. B5: Sensing — lecture notes, Department of Engineering Science, University of Oxford, Oxford, 1994.Google Scholar
14. Linn, R.J. and Hall, D.L. A survey of multisensor data fusion systems, In: SPIE, vol 1470 Data Structures and Target Classification, pp 13-29, December 1991.Google Scholar
15. Durrant-Whyte, H. B5: Estimation — lecture notes. Department of Engineering Science, University of Oxford, Oxford, 1994.Google Scholar
16. Roberts, J. Attentive Visual Tracking and Trajectory Estimation for Dynamic Scene Segmentation, PhD Thesis, Department of Aeronautics and Astronautics, University of Soudiampton, Highfield, Southampton, UK, 1994.Google Scholar
17. Alon, Y., Neilson, G. and Bui, L. Millimeter Wave Airborne Imaging Radar System. Technical Report LQ566, Lear Astronics, 1992.Google Scholar
18. Burgess, M.A. and Hayes, R.D. Synthetic vision: a view in the fog, In Proceedings of the 1 1th Digital Avionics System Conference, The Institute of Electrical and Electronic Engineers, October 1992.Google Scholar
19. Colucci, F. Safety in the oasis, Defence Helicopter, March-April 1992, pp 18–22.Google Scholar
20. Horne, W.F., Ekiert, S., Radke, J., Tucker, R.R., Harrold Han- Non, C. and Allen ZAK, J. Synthetic Vision System Technology Demonstration Methodology and Results to Date. Technical Report 921971, SAE International, 400 Commonwealth Drive, Warrendale, PA 15096-0001, USA, October 1992.Google Scholar
21. Hovanessian, S.A. Introduction to Sensor Systems, Artech House, 685 Canton Street, Norwood, MA 02062, 1988.Google Scholar
22. Pango, A. Simulation of Traffic and Collision Avoidance Systems, Masters thesis, Avionics Department, College of Aeronautics, Cran- field Institute of Technology, September 1993.Google Scholar
23. Parry, D. Military avionics advances benefit civil applications, Avionics, March 1994, pp 16–23.Google Scholar
24. Segayer, A.M. An Investigation in Mode-S Secondary Surveillance Radar, Masters thesis, Avionics Department, College of Aeronautics, Cranfield Institute of Technology, September 1993.Google Scholar
25. Sonnenberg, G.J. Radar and Electronic Navigation, Butterworths, sixth edition, 1988.Google Scholar
26. Srisuphapreeda, S. A Study of Differential GPS, Masters thesis, Avionics Department, College of Aeronautics, Cranfield Institute of Technology, September 1993.Google Scholar
27. Nougues, P.O. and Brown, D.E. Fusion of remotely sensed data and geographic information for object tracking, SPIE Vol 2233: Sensor Fusion and Aerospace Applications II, In: Nandhakumar, N. (ed), pp 12–20, April 1994.Google Scholar
28. Farina, A. and Studer, FA. Radar Data Processing, volume 1. Research Studies Press, Letchworth, Hertfordshire, England, 1985.Google Scholar
29. Laviolette, M. and Seaman, J. Jr., The efficacy of fuzzy representations of uncertainty, IEEE Transactions on Fuzzy Systems, February 1994,2, (l),pp 4–15.Google Scholar
30. Bezdek, J. Editorial: Fuzziness vs probability — again (! ?), IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), pp 1–3.Google Scholar
31. Bezdek, J. The thirsty traveller visits gamont: A rejoinder to “comments on fuzzy sets — what are they and why?”, IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), pp 43–45.Google Scholar
32. Dubois, D. and Prade, H. Fuzzy sets — a convenient fiction for modeling vagueness and possibility, IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), pp 16–21.Google Scholar
33. Hisdal, E. Interpretative versus prescriptive fuzzy set theory, IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), pp 22–26.Google Scholar
34. Klir, G.J. On the alleged superiority of probabilistic representation of uncertainty, IEEE Transactions on Fuzzy Systems, February 1994, 2,(l),pp27–31.Google Scholar
35. Kosko, B. The probability monopoly, IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), pp 32–33.Google Scholar
36. Laviolette, M. and Seaman, J. Jr. Unity and diversity of fuzziness — from a probability, IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), pp 38–42.Google Scholar
37. Lindley, D.V. Comment on “the efficacy of fuzzy representations of uncertainty”, IEEE Transactions on Fuzzy Systems, February 1994, 2,(l), p 37.Google Scholar
38. Wang, P. The interpretation of fuzziness, Center for Research on Concepts and Cognition, Indiana University, March 1993.Google Scholar
39. Wilson, N. Vagueness and bayesian probability, IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), pp 34–36.Google Scholar
40. Woodall, W.H. and Davis, R.E. Comments on “Editorial: Fuzzy models — what are they and why?”, IEEE Transactions on Fuzzy Systems, February 1994, 2, (1), p 43.Google Scholar
41. Zadeh, L. IS probability theory sufficient for dealing with uncertainty in AI? A negative view, In: Uncertainty in Artificial Intelligence, Kanal, L.N. and Lemuur, J.F. (eds), pp 103–116. Elsevier Sciences, Amsterdam, 1986.Google Scholar
42. Doyle, R.S. Multisensor data fusion for obstacle avoidance and other applications in advanced rotorcraft: Project review. Technical Note Helios/TN/6, 1994.Google Scholar