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This extensively revised second edition is a wide-ranging introduction to the concepts and ideas of multisensor data fusion, illustrated with a wealth of real-life examples. It includes 30 new sections along with an extensive list of up-to-date references.

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This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion.
It is an extensively revised second edition of the author´s successful book: "Multi-Sensor Data Fusion:
An Introduction" which was originally published by Springer-Verlag in 2007.
The main changes in the new book are:

New Material: Apart from one new chapter there are approximately 30 new sections, 50 new examples and 100 new references. At the same time, material which is out-of-date has been eliminated and the remaining text has been rewritten for added clarity. Altogether, the new book is nearly 70 pages
longer than the original book.

Matlab code: Where appropriate we have given details of Matlab code which may be downloaded from the worldwide web. In a few places, where such code is not readily available, we have included Matlab code in the body of the text.

Layout. The layout and typography has been revised. Examples and Matlab code now appear on a gray background for easy identification and advancd material is marked with an asterisk.

The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familarity with the basic tools of linear algebra, calculus and simple probability is recommended.

Although conceptually simple, the study of mult-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become 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. All too often, the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In contrast, in this book the processes are unified 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.

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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.

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