Multi-Sensor Data Fusion
An Introduction
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Beschreibung
This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended.
Although conceptually simple, the study of multi-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 this book the processes are described 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 applications and contains an extensive list of modern references. It is accompanied by a webpage from which supplementary material may be obtained, including support for course instructors and links to relevant matlab code.
The purpose of this book is to provide an introduction to the theories and techniques of multi-sensor data fusion. The book has been designed as a text for a one-semester graduate course in multi-sensor data fusion. It should also be useful to advanced undergraduates in electrical engineering or computer science who are studying data fusion for the ?rst time and to practising en- neers who wish to apply the concepts of data fusion to practical applications. The book is intended to be largely self-contained in so far as the subject of multi-sensor data fusion is concerned, although some prior exposure to the subject may be helpful to the reader. A clear understanding of multi-sensor data fusion can only be achieved with the use of a certain minimum level of mathematics.Itisthereforeassumedthatthereaderhasareasonableworking knowledge of the basic tools of linear algebra, calculus and simple probability theory. More speci?c results and techniques which are required are explained in the body of the book or in appendices which are appended to the end of the book.
Self-contained, easy accessible introduction to multi-sensor data fusion for graduate students and researchers
Well-organized modern approach to theories and techniques, includes numerous case studies that illustrate the application of techniques for multi-sensor data fusion to real problems, providing hands-on knowledge
The case studies cover a very wide range of applications – including medical imaging, geoscience applications, biometric identification, pattern classification, handwriting analysis, target tracking, computer vision etc.
Presents the first unified treatment of the subject using a Bayesian probabilistic framework
Contains details of MATLAB software programs which are available for all the multi-sensor data fusion techniques used in the book
Includes extensive modern bibliography containing more than 400 references of which more than 60% were published in the year 2000 or later
Autor*in
H.B. Mitchell
Themen in »Multi-Sensor Data Fusion«
Bayesian inference Bayesion Probabilistic Framework Computer Vision Data Fusion Multi Sensor Data Fusion Normal Sensors algorithm architecture calculus decision theory electrical engineering learning linear algebra statistics