Fills the Existing Gap of Mathematics for Data Fusion Data fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants. Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems. It tries to answer whether unified data fusion mathematics can evolve from various disparate mathematical concepts, and highlights mathematics that can add credibility to the data fusion process.
Focuses on Mathematical Tools That Use Data Fusion This text explores the use of statistical/probabilistic signal/image processing, filtering, component analysis, image algebra, decision making, and neuro-FL-GA paradigms in studying, developing and validating data fusion processes (DFP). It covers major mathematical expressions, and formulae and equations as well as, where feasible, their derivations. It also discusses SDF concepts, DF models and architectures, aspects and methods of type 1 and 2 fuzzy logics, and related practical applications. In addition, the author covers soft computing paradigms that are finding increasing applications in multisensory DF approaches and applications.
Explores the use of interval type 2 fuzzy logic and ANFIS in DF Covers the mathematical treatment of many types of filtering algorithms, target-tracking methods, and kinematic DF methods Presents single and multi-sensor tracking and fusion mathematics Considers specific DF architectures in the context of decentralized systems Discusses information filtering, Bayesian approaches, several DF rules, image algebra and image fusion, decision fusion, and wireless sensor network (WSN) multimodality fusion Data Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance. This text is geared toward researchers, scientists, teachers and practicing engineers interested and working in the multisensor data fusion area.
Jitendra R. Raol (Ramaiah Institute of Technology India)
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Introduction to Data Fusion ProcessData Fusion AspectsData Fusion ModelsSensor Data Fusion ConfigurationsSensor Data Fusion ArchitecturesData Fusion ProcessReferencesStatistics, Probability Models and Reliability: Towards Probabilistic Data FusionIntroductionStatisticsProbability ModelsProbabilistic Methods for DFReliability in DFInformation MethodsProbability Concepts for Expert System and DFProbabilistic Methods for DF: Theoretical ExamplesBayesian Formula and Sensor/DF: Illustrative ExampleReferencesFuzzy Logic and Possibility Theory-Based FusionIntroductionFuzzy Logic Type IAdaptive Neuro-fuzzy Inference SystemFuzzy Logic TypeFuzzy Intelligent Sensor FusionFL-based Procedure for Generating the Weights for a DF RuleFL-ANFIS for Parameter Estimation and Generation of DF Weights: Illustrative ExamplesPossibility TheoryFusion of Long-Wave IR and EOT Images Using Type 1 and Type 2 Fuzzy Logics: Illustrative ExamplesDF Using Dempster-Shafer and Possibility Theory: Illustrative ExampleA: Type 1 - Triangular MF-MATLAB CodeB: Type 2 - Gaussian MF-MATLAB CodeFuzzy Inference Calculations - MATLAB CodeReferencesFiltering, Target Tracking and Kinematic Data FusionIntroductionThe Kalman FilterThe Multi-Sensor Data Fusion and Kalman FilterNon-linear Data Fusion MethodsData Association in MS SystemsInformation FilteringHI Filtering-Based DFOptimal Filtering for Data Fusion with Missing MeasurementsFactorisation Filtering and Sensor DF: Illustrative ExampleReferencesDecentralised Data Fusion SystemsIntroductionData Fusion ArchitecturesDecentralised Estimation and FusionDecentralised Multi-Target TrackingMillman's Formulae in Sensor Data FusionSRIF for Data Fusion in Decentralised Network with Four Sensor Nodes: Illustrative ExampleReferencesComponent Analysis and Data FusionIntroductionIndependent Component AnalysisAn Approach to Image Fusion Using ICA BasesPrincipal Component AnalysisDiscrete-Cosine TransformWT: A Brief TheoryAn Approach to Image Fusion Using ICA and WaveletsNon-Linear ICA and PCAImage Fusion Using MR Singular Value DecompositionReferencesImage Algebra and Image FusionS. Sethu SelviIntroductionImage AlgebraPixels and Features of an ImageInverse ImageRed, Green and Blue, Grey Images and HistogramsImage SegmentationNoise Processes in an Observed/Acquired ImageImage Feature Extraction MethodsImage Transformation and Filtering ApproachesImage Fusion MathematicsImage Fusion AlgorithmsPerformance EvaluationMultimodal Biometric Systems and Fusion: Illustrative ExamplesReferencesDecision Theory and FusionIntroductionLoss and Utility FunctionsBayesian DTDecision Making with Multiple Information SourcesFuzzy Modelling Approach for Decision Analysis/FusionFuzzy-Evolutive Integral ApproachDecision Making Based on VotingDeF Using FL for Aviation ScenariosDeF StrategiesSA with FL and DeF for Aviation Scenarios: Illustrative ExamplesReferencesWireless Sensor Networks and Multimodal Data FusionIntroductionCommunication Networks and Their Topologies in WSNsSensor/Wireless Sensor NetworksWireless Sensor Networks and ArchitecturesSensor Data Fusion in WSNMultimodality Sensor FusionDecision Fusion Rules in WSNData Aggregation in WSNHybrid Data and Decision Fusion in WSNOptimal Decision Fusion in WSNReferencesSoft Computing Approaches to Data FusionIntroductionArtificial Neural NetworksRadial Basis Function Neural NetworkRecurrent Neural NetworksFL and Systems as SC ParadigmFL in Kalman Filter for Image-Centroid Tracking: A Type of FusionGenetic AlgorithmsSDF Approaches Using SC Methods: Illustrative ExamplesMachine LearningNeural-Fuzzy-Genetic Algorithm FusionImage Analysis Using ANFIS: Illustrative ExampleAcknowledgementReferencesA: Some Algorithms and/or Their DerivationsB: Other Methods of DF and Fusion Performance Evaluation MetricsC:Automatic Data FusionD: Notes and Information on Data Fusion Software ToolsE: Definitions of Sensor DF in LiteratureF: Some Current Research Topics in DF
Jitendra R. Raol received a BE and ME in electrical engineering from the MS University of Baroda, Vadodara in 1971 and 1973, respectively, and a PhD (in electrical and computer engineering) from McMaster University, Hamilton, Canada in 1986. He taught for two years at the MS University of Baroda before joining the National Aeronautical Laboratory in 1975. He retired in 2007 as Scientist G and head, flight mechanics and control division at CSIR-NAL. His main research interests are DF, system identification, state/parameter estimation, flight mechanics-flight data analysis, H-infinity filtering, ANNs, fuzzy systems, genetic algorithms, and soft technologies for robotics.
An application's guide to sensor fusion - Raol's comprehensive yet succinct handling of the mathematical fundamentals of sensor fusion make this a reference source for every practitioner. -Ajith K. Gopal, The Council for Scientific and Industrial Research in South Africa ... comprehensively presents tools for data fusions. Initial two chapters cover basic of data fusion and state estimations, especially Bayesian framework. The rest of chapters deal with advance topics that include fuzzy-logic based design, centralized and decentralized strategies, and image fusion. I feel the content of the book will useful both academia and industry. -Dr. Mangal Kothari, Indian Institute of Technology Kanpur