LEADER 05583nam 2200697 450 001 9910815823603321 005 20230803203714.0 010 $a1-118-57867-8 010 $a1-118-57863-5 010 $a1-118-57857-0 035 $a(CKB)3710000000187039 035 $a(EBL)1734296 035 $a(SSID)ssj0001340563 035 $a(PQKBManifestationID)11784091 035 $a(PQKBTitleCode)TC0001340563 035 $a(PQKBWorkID)11380297 035 $a(PQKB)10555080 035 $a(OCoLC)889305696 035 $a(MiAaPQ)EBC1734296 035 $a(Au-PeEL)EBL1734296 035 $a(CaPaEBR)ebr10892217 035 $a(CaONFJC)MIL627055 035 $a(OCoLC)884014933 035 $a(EXLCZ)993710000000187039 100 $a20140721h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUncertainty theories and multisensor data fusion /$fAlain Appriou 210 1$aLondon, [England] ;$aHoboken, New Jersey :$cISTE :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (278 p.) 225 1 $aInstrumentation and measurement series 300 $aDescription based upon print version of record. 311 $a1-84821-354-9 320 $aIncludes bibliographical references and index. 327 $aCover; 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 327 $a2.3. 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 327 $a3.4. 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 327 $a5.4. 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 327 $a6.4. 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 327 $a7.5. Exploitation of the diversity of information sources: classification on the basis of distinct but overlapping sets 330 $aAddressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first 410 0$aInstrumentation and measurement series. 606 $aMultisensor data fusion 606 $aMultisensor data fusion$vCongresses 606 $aMultisensor data fusion$vHandbooks, manuals, etc 615 0$aMultisensor data fusion. 615 0$aMultisensor data fusion 615 0$aMultisensor data fusion 676 $a623.042 700 $aAppriou$b Alain$01722167 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910815823603321 996 $aUncertainty theories and multisensor data fusion$94122322 997 $aUNINA