Uncertainty theories and multisensor data fusion

Uncertainty theories and multisensor data fusion / Alain Appriou.

Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the s...

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Bibliographic Details
Main Author: Appriou, Alain (Author)
Format: eBook
Language:English
Published: London [England] ; Hoboken, New Jersey : ISTE : Wiley, 2014.
Series:Instrumentation and measurement series.
Subjects:
Multisensor data fusion.
Multisensor data fusion > Congresses.
Multisensor data fusion > Handbooks, manuals, etc.
TECHNOLOGY & ENGINEERING > Mechanical.
Multisensor data fusion
handbooks.
Conference papers and proceedings
Handbooks and manuals
Handbooks and manuals.
Guides et manuels.
Online Access:Click for online access
Table of Contents:
  • Cover; Title Page; Copyright; Contents; Introduction; Chapter 1. Multisensor Data Fusion; 1.1. Issues at stake; 1.2. Problems; 1.2.1. Interpretation and modeling of data; 1.2.2. Reliability handling; 1.2.3. Knowledge propagation; 1.2.4. Matching of ambiguous data; 1.2.5. Combination of sources; 1.2.6. Decision-making; 1.3. Solutions; 1.3.1. Panorama of useful theories; 1.3.2. Process architectures; 1.4. Position of multisensor data fusion; 1.4.1. Peculiarities of the problem; 1.4.2. Applications of multisensor data fusion; Chapter 2. Reference Formalisms; 2.1. Probabilities; 2.2. Fuzzy sets.
  • 2.3. Possibility theory2.4. Belief functions theory; 2.4.1. Basic functions; 2.4.2. A few particularly useful cases; 2.4.3. Conditioning/deconditioning; 2.4.4. Refinement/coarsening; Chapter 3. Set Management and Information Propagation; 3.1. Fuzzy sets: propagation of imprecision; 3.2. Probabilities and possibilities: the same approach to uncertainty; 3.3. Belief functions: an overarching vision in terms of propagation; 3.3.1. A generic operator: extension; 3.3.2. Elaboration of a mass function with minimum specificity; 3.3.3. Direct exploitation of the operator of extension.
  • 3.4. Example of application: updating of knowledge over timeChapter 4. Managing The Reliability of Information; 4.1. Possibilistic view; 4.2. Discounting of belief functions; 4.3. Integrated processing of reliability; 4.4. Management of domains of validity of the sources; 4.5. Application to fusion of pixels from multispectral images; 4.6. Formulation for problems of estimation; Chapter 5. Combination of Sources; 5.1. Probabilities: a turnkey solution, Bayesian inference; 5.2. Fuzzy sets: a grasp of axiomatics; 5.3. Possibility theory: a simple approach to the basic principles.
  • 5.4. Theory of belief functions: conventional approaches5.5. General approach to combination: any sets and logics; 5.6. Conflict management; 5.7. Back to Zadeh's paradox; Chapter 6. Data Modeling; 6.1. Characterization of signals; 6.2. Probabilities: immediate taking into account; 6.3. Belief functions: an open-ended and overarching framework; 6.3.1. Integration of data into the fusion process; 6.3.2. Generic problem: modeling of Cij values; 6.3.3. Modeling measurements with stochastic learning; 6.3.4. Modeling measurements with fuzzy learning; 6.3.5. Overview of models for belief functions.
  • 6.4. Possibilities: a similar approach6.5. Application to a didactic example of classification; Chapter 7. Classification: Decision-Making And Exploitation of the Diversity of Information Sources; 7.1. Decision-making: choice of the most likely hypothesis; 7.2. Decision-making: determination of the most likely set of hypotheses; 7.3. Behavior of the decision operator: some practical examples; 7.4. Exploitation of the diversity of information sources: integration of binary comparisons.

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