Multi-Sensor Data Fusion

An Introduction

  • Authors
  • H.B.¬†Mitchell

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Basics

    1. Front Matter
      Pages 1-1
    2. H.B. Mitchell
      Pages 3-13
    3. H.B. Mitchell
      Pages 15-28
    4. H.B. Mitchell
      Pages 29-44
  3. Representation

    1. Front Matter
      Pages 45-45
    2. H.B. Mitchell
      Pages 47-67
    3. H.B. Mitchell
      Pages 69-82
    4. H.B. Mitchell
      Pages 83-95
    5. H.B. Mitchell
      Pages 97-112
  4. Data Fusion

    1. Front Matter
      Pages 113-113
    2. H.B. Mitchell
      Pages 115-131
    3. H.B. Mitchell
      Pages 133-153
    4. H.B. Mitchell
      Pages 155-171
    5. H.B. Mitchell
      Pages 173-200
    6. H.B. Mitchell
      Pages 201-219
    7. H.B. Mitchell
      Pages 221-240
  5. Sensor Management

    1. Front Matter
      Pages 241-241
    2. H.B. Mitchell
      Pages 243-248
  6. Appendices

    1. Front Matter
      Pages 249-249

About this book

Introduction

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.

Keywords

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

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-71559-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-71463-7
  • Online ISBN 978-3-540-71559-7
  • About this book