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

Authors:
Jaime Esteban;Andrew Starr;Robert Willetts;Paul Hannah;Peter Bryanston-Cross
Affiliations:
School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, M60 1QD, Manchester, UK;School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, M60 1QD, Manchester, UK;School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, M60 1QD, Manchester, UK;School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Sackville Street, M60 1QD, Manchester, UK;School of Engineering, University of Warwick, Sackville Street, CV4 7AL, Coventry, UK
Venue:
Neural Computing and Applications
Year:
2005

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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.