Multisensor Fusion

1. Front Matter

   Pages i-xi

   PDF

2. Introduction and Fundamentals

   1. Information Fusion in the Human Brain

      • R. Von Hanwehr

      Pages 1-36

   2. Fundamentals of Reasoning and Multisensing

      • E. Waltz

      Pages 37-70

   3. Introduction to DF: Models and Processes, Architectures, Techniques and Applications

      • E. Shahbazian

      Pages 71-97

   4. The Fusion of Decisions for Distributed Recognition

      • M. D. Bedworth

      Pages 99-124

   5. Reexamining Data Fusion Processing at Levels 2, 3, and 4

      • J. Llinas

      Pages 125-161

3. Multisensor Fusion Theory

   1. An Introduction to Distributed Detection Theory

      • P. K. Varshney

      Pages 163-182

   2. Distributed Sensor Networks and Neural Trees for Multisensor Data Fusion in Computer Vision

      • G. L. Foresti

      Pages 183-196

   3. Architectures for Efficient Data Fusion

      • P. Vangasse, D. Nicholson

      Pages 197-221

   4. Reasoning Frameworks

      • P. Valin

      Pages 223-245

   5. Random Sets and Unification

      • P. Valin

      Pages 247-266

   6. Fusion of Information under Imprecision and Uncertainty, Numerical Methods, and Image Information Fusion

      • I. Bloch

      Pages 267-293

   7. Multisensor Fusion under Unknown Distributions Finite-Sample Performance Guarantees

      • N. S. V. Rao

      Pages 295-329

   8. Data Association and Multitarget Tracking

      • J.-P. Le Cadre

      Pages 331-349

   9. Wavelets for Modeling and Data Fusion in Remote Sensing

      • T. Ranchin

      Pages 351-363

   10. Soft Sensor Management for Multisensor Tracking Algorithm

       • V. Nimier

       Pages 365-379

   11. Intellectualization of Multi-Sensor Systems for Decision Making Process

       • T. E. Bekmuratov

       Pages 381-385

4. Multisensor Design and Implementation

   1. Implementing Data Fusion Systems

      • R. Reynaud

      Pages 387-418

   2. The Implementation of Data Fusion Systems

      • D. L. Hall

      Pages 419-433

   3. Multisensor Data Fusion Using Kalman Filters Based on Neuro-Fuzzy Linearisation

      • C. Harris, Q. Gan

      Pages 435-456

For some time, all branches of the military have used a wide range of sensors to provide data for many purposes, including surveillance, reconnoitring, target detection and battle damage assessment. Many nations have also attempted to utilise these sensors for civilian applications, such as crop monitoring, agricultural disease tracking, environmental diagnostics, cartography, ocean temperature profiling, urban planning, and the characterisation of the Ozone Hole above Antarctica. The recent convergence of several important technologies has made possible new, advanced, high performance, sensor based applications relying on the near-simultaneous fusion of data from an ensemble of different types of sensors. The book examines the underlying principles of sensor operation and data fusion, the techniques and technologies that enable the process, including the operation of 'fusion engines'. Fundamental theory and the enabling technologies of data fusion are presented in a systematic and accessible manner. Applications are discussed in the areas of medicine, meteorology, BDA and targeting, transportation, cartography, the environment, agriculture, and manufacturing and process control.

• algorithms
• classification
• computer vision
• fuzzy
• image processing
• information
• navigation
• optimization
• sensing
• uncertainty
• verification

• University of Notre Dame, Notre Dame, USA

  A. K. Hyder

• Lockheed-Martin Canada, Montreal, Canada

  E. Shahbazian

• Veridian Systems, Ann Arbor, USA

  E. Waltz

Policies and ethics