Sensor and data fusion concepts and applications

Author(s)

    • Klein, Lawrence A.

Bibliographic Information

Sensor and data fusion concepts and applications

Lawrence A. Klein

(Tutorial texts in optical engineering, v. TT 35)

SPIE, c1999

2nd ed

  • :pbk

Available at  / 4 libraries

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Note

Includes bibliographical references and index

Description and Table of Contents

Description

First published in 1993, this "Tutorial Text" has been revised and updated to provide explanations and examples of data fusion algorithms in areas not covered in the first edition. These include Bayesian inference, artificial neural networks and fuzzy logic. All of the chapters in the first edition have been revised and updated and new material is included on the FASCODE and MODTRAN atmospheric models, and EOSAEL, which analyzes physical processes that affect the performance of millimeter-wave and IR sensors.

Table of Contents

  • Multiple Sensor System Applications, Benefits, and Atmospheric Attenuation
  • Data Fusion Algorithms and Architectures
  • Bayesian Inference
  • Dempster-Shafer Algorithm
  • Artificial Neural Networks
  • Voting Fusion
  • Fuzzy Logic and Neural Networks
  • Passive Data Association Techniques for Unambiguous Location of Targets. Appendices: Planck Radiation Law and Radiative Transfer
  • Voting Fusion With Nested Confidence Levels.

by "Nielsen BookData"

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