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Multi-Sensor Data Fusion

An Introduction

  • Book
  • © 2007

Overview

Authors:
  1. H.B. Mitchell
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  • 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

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Table of contents (16 chapters)

  1. Front Matter

    Pages I-XIV
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  2. Basics

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • H.B. Mitchell
      Pages 3-13

    3. Sensors

      • H.B. Mitchell
      Pages 15-28

    4. Architecture

      • H.B. Mitchell
      Pages 29-44

  3. Representation

    1. Front Matter

      Pages 45-45
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    2. Common Representational Format

      • H.B. Mitchell
      Pages 47-67

    3. Spatial Alignment

      • H.B. Mitchell
      Pages 69-82

    4. Temporal Alignment

      • H.B. Mitchell
      Pages 83-95

    5. Sensor Value Normalization

      • H.B. Mitchell
      Pages 97-112

  4. Data Fusion

    1. Front Matter

      Pages 113-113
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    2. Bayesian Inference

      • H.B. Mitchell
      Pages 115-131

    3. Parameter Estimation

      • H.B. Mitchell
      Pages 133-153

    4. Robust Statistics

      • H.B. Mitchell
      Pages 155-171

    5. Sequential Bayesian Inference

      • H.B. Mitchell
      Pages 173-200

    6. Bayesian Decision Theory

      • H.B. Mitchell
      Pages 201-219

    7. Ensemble Learning

      • H.B. Mitchell
      Pages 221-240

  5. Sensor Management

    1. Front Matter

      Pages 241-241
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    2. Sensor Management

      • H.B. Mitchell
      Pages 243-248

  6. Appendices

    1. Front Matter

      Pages 249-249
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Keywords

About this book

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.

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