Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion (DF) and provides a proper and adequate understanding of the basic mathematics directly related to DF.

This new edition offers updated chapters alongside four new chapters that are based on recent research carried out by the authors, including topics on machine learning techniques, target localization using a network of 2D ground radar, thermal imaging sensors for multi-target angle-only tracking, and multi-sensor data fusion for a single platform and team platforms. This book also covers major mathematical expressions, formulae and equations, and, where feasible, their derivations. It discusses signed distance function concepts, DF models and architectures, aspects and methods of types 1 and 2 fuzzy logics, and related practical applications. In addition, the authors cover soft computing paradigms that are finding increasing applications in multi-sensory DF approaches and applications.

This text is geared toward researchers, scientists, teachers, and practicing engineers interested in and working in the multi-sensor data fusion area.