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Mathematics of Data Fusion

  • Book
  • © 1997

Overview

Authors:
  1. I. R. Goodman

    1. NCCOSC RDTE DIV, San Diego, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Ronald P. S. Mahler

    1. Lockheed Martin Tactical Defences Systems, Saint Paul, USA

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    You can also search for this author in PubMed   Google Scholar

  3. Hung T. Nguyen

    1. Department of Mathematical Sciences, New Mexico State University, Las Cruces, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Theory and Decision Library B (TDLB, volume 37)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Introduction

    1. Introduction

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 1-14

  3. Introduction to Data Fusion

    1. Front Matter

      Pages 15-16
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    2. Data Fusion and Standard Techniques

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 17-89

  4. The Random Set Approach to Data Fusion

    1. Front Matter

      Pages 91-91
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    2. Foundations of Random Sets

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 93-129

    3. Finite Random Sets

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 131-173

    4. Finite-Set Statistics

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 175-218

    5. Fusion of Unambiguous Observations

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 219-262

    6. Fusion of Ambiguous Observations

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 263-293

    7. Output Measurement

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 295-338

  5. Use of Conditional and Relational Events in Data Fusion

    1. Front Matter

      Pages 339-344
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    2. Introduction to the Conditional and Relational Event Algebra Aspects of Data Fusion

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 345-358

    3. Potential Application of Conditional Event Algebra to Combining Conditional Information

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 359-367

    4. Three Particular Conditional Event Algebras

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 369-382

    5. Further Development of Product Space Conditional Event Algebra

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 383-403

    6. Product Space Conditional Event Algebra as a Tool for Further Analysis of Conditional Event Algebra Issues

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 405-423

    7. Testing of Hypotheses for Distinctness of Events and Event Similarity Issues

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 425-454

    8. Testing Hypotheses And Estimation Relative To Natural Language Descriptions

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 455-480

    9. Development of Relational Event Algebra Proper to Address Data Fusion Problems

      • I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen
      Pages 481-501
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Keywords

About this book

Data fusion or information fusion are names which have been primarily assigned to military-oriented problems. In military applications, typical data fusion problems are: multisensor, multitarget detection, object identification, tracking, threat assessment, mission assessment and mission planning, among many others. However, it is clear that the basic underlying concepts underlying such fusion procedures can often be used in nonmilitary applications as well. The purpose of this book is twofold: First, to point out present gaps in the way data fusion problems are conceptually treated. Second, to address this issue by exhibiting mathematical tools which treat combination of evidence in the presence of uncertainty in a more systematic and comprehensive way. These techniques are based essentially on two novel ideas relating to probability theory: the newly developed fields of random set theory and conditional and relational event algebra.
This volume is intended to be both an update on research progress on data fusion and an introduction to potentially powerful new techniques: fuzzy logic, random set theory, and conditional and relational event algebra.
Audience: This volume can be used as a reference book for researchers and practitioners in data fusion or expert systems theory, or for graduate students as text for a research seminar or graduate level course.

Authors and Affiliations

  • NCCOSC RDTE DIV, San Diego, USA

    I. R. Goodman

  • Lockheed Martin Tactical Defences Systems, Saint Paul, USA

    Ronald P. S. Mahler

  • Department of Mathematical Sciences, New Mexico State University, Las Cruces, USA

    Hung T. Nguyen

Bibliographic Information

  • Book Title: Mathematics of Data Fusion

  • Authors: I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen

  • Series Title: Theory and Decision Library B

  • DOI: https://doi.org/10.1007/978-94-015-8929-1

  • Publisher: Springer Dordrecht

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media B.V. 1997

  • Hardcover ISBN: 978-0-7923-4674-6Published: 31 August 1997

  • Softcover ISBN: 978-90-481-4887-5Published: 07 December 2010

  • eBook ISBN: 978-94-015-8929-1Published: 14 March 2013

  • Edition Number: 1

  • Number of Pages: XII, 508

  • Topics: Applications of Mathematics, Artificial Intelligence, Statistics, general

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