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A Review of data fusion models and architectures: towards engineering guidelines

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Abstract

This paper reviews the potential benefits that can be obtained by the implementation of data fusion in a multi-sensor environment. A thorough review of the commonly used data fusion frameworks is presented together with important factors that need to be considered during the development of an effective data fusion problem-solving strategy. A system-based approach is defined for the application of data fusion systems within engineering. Structured guidelines for users are proposed.

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Acknowledgements

This work was supported by the INTErSECT Faraday Partnership and EPSRC as part of project GR/M44484 “The application of data fusion to a multi sensored intelligent engine”. The authors gratefully acknowledge the assistance of the following partners: Corus, National Physical Laboratory, QinetiQ, Rolls-Royce, and Wolfson Maintenance; and particularly of Dr Mark Bedworth, Mr Graham Hesketh, Prof. John Macintyre, and Mrs Jane O’Brien in the preparation of the guidelines.

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Authors and Affiliations

  1. School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, Manchester, M60 1QD, UK

    Jaime Esteban, Andrew Starr, Robert Willetts & Paul Hannah

  2. School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

    Peter Bryanston-Cross

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  1. Jaime Esteban
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  2. Andrew Starr
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  3. Robert Willetts
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  4. Paul Hannah
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  5. Peter Bryanston-Cross
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Esteban, J., Starr, A., Willetts, R. et al. A Review of data fusion models and architectures: towards engineering guidelines. Neural Comput & Applic 14, 273–281 (2005). https://doi.org/10.1007/s00521-004-0463-7

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  • DOI: https://doi.org/10.1007/s00521-004-0463-7

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