Multi-Source Information Fusion Models and Methods Based on Granular Computing

Abstract

The advantages of the theory of multi-source information fusion (MSIF) lie in its provision of an effective approach to integrate, analyse, and utilize information from multiple diverse sources, facilitating improved decision-making, problem-solving, and information analysis. Granular computing (GrC) is a theoretical, technical, and methodological approach within the current field of intelligent information processing and granularity modelling, aiming to simulate human-like problem-solving. Combining both approaches can enhance the efficiency and quality of fusion. This chapter presents several solutions that combine multi-source information fusion with GrC, including the multi-source homogeneous fusion model and heterogeneous data fusion method. The introduction of these methods contributes to improved handling of uncertainty, fuzziness, and multi-source complexity issues, thereby enhancing the capabilities of information processing and decision support.