Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-29T14:20:10.910Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-sensor data fusion in defence and aerospace

Published online by Cambridge University Press:  04 July 2016

C. J. Harris ,
A. Bailey  and
T.J. Dodd
C. J. Harris
Affiliation:
Image, Speech and Intelligent Systems Research Group , University of Southampton Southampton, UK
A. Bailey
Affiliation:
Image, Speech and Intelligent Systems Research Group , University of Southampton Southampton, UK
T.J. Dodd
Affiliation:
Image, Speech and Intelligent Systems Research Group , University of Southampton Southampton, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

The UK OST Technology Foresight for defence and aerospace identified multi-sensor data fusion as a future critical enabling technology for the UK, requiring a coordinated research agenda. This review paper provides an overview of past research and applications of data fusion. The process of data fusion and sensor integration is formally introduced together with a variety of implementation architectures, that recognise data fusion as a critical element in overall systems integration. The various benefits and attributes of data fusion are discussed together with a brief review of potentially fruitful areas of future research.

Type
Research Article
Information
The Aeronautical Journal , Volume 102 , Issue 1015 , May 1998 , pp. 229 - 244
Copyright
Copyright © Royal Aeronautical Society 1998 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Defence and Aerospace Foresight Technology Working Party. Data Fusion and Data Processing. Royal Aeronautical Society, 1997. ISBN 1857680685.Google Scholar
2. Clark, J.J. and Yuille, A.L. Data Fusion for Sensory Information Processing Systems, Kluwer International Series in Engineering and Computer Science, Kluwer Academic Publishers, 1990.Google Scholar
3. Manyika, J. and Durrant-whyte, H.F. Data fusion and sensor man agement: a decentralized information-theoretic approach, Series in Electrical and Electronic Engineering, Ellis Horwood, 1994.Google Scholar
4. Waltz, E. and Llinas, J. Multisensor Data Fusion, Artech House, 1990.Google Scholar
5. Harris, C.J. (Ed) Application of Artificial Intelligence to Command & Control Systems, IEE Computing Series 13, Peter Peregrinus, 1988.Google Scholar
6. Miles, J.A.H. Artificial Intelligence Applied to Data Fusion and Situation Assessment for Command and Control, PhD Thesis, University of Southampton, 1988.Google Scholar
7. Bossley, K.M. Neurofuzzy Modelling Approaches in System Identification, PhD Thesis, University of Southampton, 1997.Google Scholar
8. Waltz, E.L. Data Fusion for C3I: A Tutorial. In Command,Control, Communications Intelligence (C3I).Handbook, pages 217–226, California, 1986. EW Communications Inc.Google Scholar
9. Hall, D.L. Mathematical Techniques in Multisensor Data Fusion,Artech House, 1992.Google Scholar
10. Bridgett, N.A. and Harris, C.J. Neurofuzzy identification and control of a gas turbine jet engine. In Int Symp on Signal Processing, Robotics and Neural Networks, Lille, France, 1994. IMACS.Google Scholar
11. Korona, Z. and Kokar, M.M. A Fusion and Learning Algorithm for Landing Aircraft Tracking: Compensating for Exhaust Plume Disturbance, IEEE Transactions on Aerospace and Electronic Systems, AC-31,(3), July 1995, pp 1210–1215.Google Scholar
12. Stevens, M.R. and Beveridge, J.R. Precise matching of 3D target models to multisensor data, IEEE Transactions on Image Processing,January 1997, 6, (1), pp 126–142.Google Scholar
13. Miller, M.I., Grenander, U., O'sullivan, J.A. and Snyder, D.L. Automatic target recognition organized via jump-diffusion algorithms,IEEE Transactions on Image Processing, January 1997, 6, (1), pp 157–174.Google Scholar
14. Casasent, D. Detection filters and algorithm fusion for ATR, IEEE Transactions on Image Processing, January 1997, 6, (1), pp 114–125.Google Scholar
15. Yu, X., Hoff, L.E., Reed, I.S., Chen, A.M. and Stotts., L.B Automatic target detection and recognition in multiband imagery: a unified ML detection and estimation approach, IEEE Transactions on Image Processing, January 1997, 6, (1), pp 143–156.Google Scholar
16. Shetty, S. and Alouani, AT. A Multisensor tracking system with an image-based maneuver detector, IEEE Transactions on Aerospace and Electronic Systems, AC-32, January 1995, (1), pp 167–181.Google Scholar
17. Hong, L., Wang, W-C, Logan, M. and Donohue, T. Multiplatform multisensor fusion with adaptive-rate data communication, IEEE Transactions on Aerospace and Electronic Systems, January 1997, AC-33, (l),pp 274–281.Google Scholar
18. Baek, W. and Bommareddy, S. Optimal m-ary data fusion with distributed sensors, IEEE Transactions on Aerospace and Electronic Systems, July 1995, AC-31, (3), pp 1150–1152. 14.Google Scholar
19. Martinerie, F. Data fusion and tracking using HMMs in a distributed sensor network, IEEE Transactions on Aerospace and Electronic Systems, January 1997, AC-33, (1), pp 11–28.Google Scholar
20. Kokar, M. and Kim, K. Preface to the special section on data fusion: Architectures and issues, Cont Eng Practice, 1994, 2, (5), pp 803–809.Google Scholar
21. Doyle, R.S. Neurofuzzy Multi-Sensor Data Fusion for Helicopter Obstacle Avoidance, PhD Thesis, University of Southampton, 1997.Google Scholar
22. Luo, R. and Kay, M. Multisensor Integration and Fusion in Intelligent Systems, IEEE Transactions on Systems, Man and Cybernetics, 1989, 19, (5), pp 901–931.Google Scholar
23. Grime, S. and Durrant-whyte, H.F. Data fusion in decentralised sensor networks, Cont Eng Practice, 1994, 2, (5), pp 849–863.Google Scholar
24. Fraser, R.J.C. and Corfield, S.J. Proteus: Technical Overview Report,Proteus Project, Volumes 1 and 2, ISIS Research Group, University of Southampton, and USI Department, DRA Bincleaves, 1993.Google Scholar
25. Fraser, R.J.C. Embedded Command and Control Infrastructures for Intelligent Autonomous Systems, PhD Thesis, University of Southampton, 1994.Google Scholar
26. Shafer, S.A., Stentz, A. and Thorpe, C.E. An architecture for sensor fusion in a mobile robot, Proceedings of IEEE International Conference on Robotics and Automation, IEEE, 1986, pp 2002–2011.Google Scholar
27. Bar-shalom, Y. (Ed) Multitarget-Multisensor Tracking: Advanced Applications, Artech House, 1990.Google Scholar
28. Blackman, S.S. Multiple-Target Tracking with Radar Applications, Artech House, 1986.Google Scholar
29. Chui, C.K. and Chen, G. Kalrnan Filtering with Real-Time Applications, Vol 17 of Springer Series in Information Sciences, Springer-Ver-lag, 1987.Google Scholar
30. Schlee, F.H., Standish, C.J. and Toda, N.F. Divergence in the Kalman filter, A1AA J, June 1967,5, (6), pp 1114–1120.Google Scholar
31. Fitzgerald, R.J. Divergence of the Kalman filter, IEEE Transactions on Automatic Control, December 1971, AC-16, (6), pp 736–747.Google Scholar
32. Jazwinski, A.J. Limited memory optimal filtering, IEEE Transactions on Automatic Control, October 1968, AC-13, pp 558–563.Google Scholar
33. Jazwinski, A.J. Adaptive filtering, Automatica, July 1969, 5, pp 475–485.Google Scholar
34. Bekir, E. Adaptive Kalman filter for tracking maneuvering targets, J Guidance, Cont & Dynamics, Sept-Oct 1983, 6, (5), pp 414–416.Google Scholar
35. Mehra, R.K. On the identification of variances and adaptive Kalman filtering, IEEE Transactions on Automatic Control, 1970, AC-15, (2), pp 175–184.Google Scholar
36. Mehra, R.K. Approaches to adaptive filtering, IEEE Transactions on Automatic Control, 1972, AC-17, (10), pp 693–698.Google Scholar
37. Wang, H.P., Kung, M.C. and Lin, T.Y. Multi-model adaptive Kalman filter design for manoeuvring target tracking, Int J Systems Science, 1994,25, (11), pp 2039–2046.Google Scholar
38. Grover brown, R. A new look at the Magill adaptive filter as a practical means of multiple hypothesis testing, IEEE Transactions on Circuits and Systems, October 1983, CAS-30, (10), pp 765–768.Google Scholar
39. Brown, M. and Harris, C.J. Neurofuzzy Adaptive Modelling and Control, Prentice Hall International Series in Systems and Control Engineering, Prentice Hall, 1994.Google Scholar
40. Roberts, J.M., Mills, D.J., Charnley, D. and Harris, C.J. Improved Kalman filter initialisation using neurofuzzy estimation, In 4th International Conference on Artificial Neural Networks, IEE, Cambridge, UK, 1995, pp 329–334.Google Scholar
41. Doyle, R.S. and Harris, C.J. Multi-sensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms, Aeronaut J, June/July 1996,100, (996), pp 241–251.Google Scholar
42. Bishop, CM. Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.Google Scholar
43. Wu, Z.Q. and Harris, C.J. A neurofuzzy network structure for modelling and state estimation of unknown nonlinear systems, Int J Systems Science, 1997,28, (4), pp 335–345.Google Scholar
44. French, M. and Rogers, E. A stability analysis of direct neuro-fuzzy controllers, In Control ‘96, 1996, pp 182–187.Google Scholar
45. Sugeno, M. and Tanaka, K. Successive identification of a fuzzy model and its applications to prediction of a complex system, Fuzzy Sets and Systems, 1991, 42, pp 315–334.Google Scholar
46. Dubois, D. and Prade, H. Possibility theory and data fusion in poorly informed environments, Cont Eng Practice, 1994, 2, (5), pp 811–823.Google Scholar