Data Fusion Mathematics

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This book addresses the mathematical foundations essential for data fusion (DF), which integrates vast amounts of information from diverse sources through algorithmic and intelligent methods, including artificial intelligence. Known as sensor data fusion (SDF), this system is crucial for applications in vehicle monitoring, aerospace, large structures, and industrial automation. It provides a thorough overview of the mathematics related to DF, enabling the evaluation of performance in designed systems. The text investigates whether a unified mathematical framework can emerge from various concepts, enhancing the credibility of the data fusion process. It delves into statistical and probabilistic signal/image processing, filtering, component analysis, decision-making, and neuro-FL-GA paradigms for developing and validating DF processes. Key mathematical expressions, formulae, and their derivations are included, alongside discussions of SDF concepts, DF models, fuzzy logics, and practical applications. The author also explores soft computing paradigms increasingly relevant in multisensory DF applications. Topics include interval type 2 fuzzy logic, ANFIS, filtering algorithms, target-tracking methods, decentralized DF architectures, and Bayesian approaches. This comprehensive resource is aimed at researchers, scientists, educators, and engineers engaged in multisensor data fusion.