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A review of data fusion models and systems

Implementation of a data fusion system is a complex task. Several of the most critical issues related to the implementation are requirements analysis, sensor selection, architecture selection, algorithm selection, software implementation and testing and evaluation. A number of data fusion frameworks have been developed to serve the purpose. This article reviews the literature on data fusion models, as well as aspects of systems engineering related to multisensory fusion. A novel generic framework is proposed to link data fusion system engineering with algorithm engineering paradigm. A new term called ‘impact factor’ is introduced to designate data fusion performance research findings.