Introduction -- Sensors -- Architecture -- Common Representational Format -- Spatial Alignment -- Temporal Alignment -- Semantic Alignment -- Radiometric Normalization -- Bayesian Inference -- Parameter Estimation -- Robust Statistics -- Sequential Bayesian Inference -- Bayesian Decision Theory -- Ensemble Learning -- Sensor Management.

“Data Fusion: Concepts and Ideas” provides a comprehensive introduction to the concepts and idea of multisensor data fusion. This textbook is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion: An Introduction". The book is self-contained and no previous knowledge of multi-sensor data fusion is assumed. The reader is made familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory which are combined by using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident. The book is illustrated with many real-life examples taken from a diverse range of applications and contains an extensive list of modern references. The new completely revised and updated edition includes nearly 70 pages of new material including a full new chapter as well as approximately 30 new sections, 50 new examples and 100 new references as well as additional Matlab code where appropriate.