05583nam 2200697 450 991081582360332120230803203714.01-118-57867-81-118-57863-51-118-57857-0(CKB)3710000000187039(EBL)1734296(SSID)ssj0001340563(PQKBManifestationID)11784091(PQKBTitleCode)TC0001340563(PQKBWorkID)11380297(PQKB)10555080(OCoLC)889305696(MiAaPQ)EBC1734296(Au-PeEL)EBL1734296(CaPaEBR)ebr10892217(CaONFJC)MIL627055(OCoLC)884014933(EXLCZ)99371000000018703920140721h20142014 uy 0engur|n|---|||||txtccrUncertainty theories and multisensor data fusion /Alain AppriouLondon, [England] ;Hoboken, New Jersey :ISTE :Wiley,2014.©20141 online resource (278 p.)Instrumentation and measurement seriesDescription based upon print version of record.1-84821-354-9 Includes bibliographical references and index.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 sets2.3. 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 extension3.4. 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 principles5.4. 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 functions6.4. 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 comparisons7.5. Exploitation of the diversity of information sources: classification on the basis of distinct but overlapping setsAddressing 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 specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the firstInstrumentation and measurement series.Multisensor data fusionMultisensor data fusionCongressesMultisensor data fusionHandbooks, manuals, etcMultisensor data fusion.Multisensor data fusionMultisensor data fusion623.042Appriou Alain1722167MiAaPQMiAaPQMiAaPQBOOK9910815823603321Uncertainty theories and multisensor data fusion4122322UNINA