1st Edition

Data Fusion Mathematics Theory and Practice

By Jitendra R. Raol Copyright 2016
    504 Pages 70 B/W Illustrations
    by CRC Press

    600 Pages 122 B/W Illustrations
    by CRC Press

    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.

    This book:

    • 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.

    Introduction to Data Fusion Process
    Data Fusion Aspects
    Data Fusion Models
    Sensor Data Fusion Configurations
    Sensor Data Fusion Architectures
    Data Fusion Process
    Statistics, Probability Models and Reliability: Towards Probabilistic Data Fusion
    Probability Models
    Probabilistic Methods for DF
    Reliability in DF
    Information Methods
    Probability Concepts for Expert System and DF
    Probabilistic Methods for DF: Theoretical Examples
    Bayesian Formula and Sensor/DF: Illustrative Example
    Fuzzy Logic and Possibility Theory-Based Fusion
    Fuzzy Logic Type I
    Adaptive Neuro-fuzzy Inference System
    Fuzzy Logic Type
    Fuzzy Intelligent Sensor Fusion
    FL-based Procedure for Generating the Weights for a DF Rule
    FL-ANFIS for Parameter Estimation and Generation of DF Weights: Illustrative Examples
    Possibility Theory
    Fusion of Long-Wave IR and EOT Images Using Type 1 and Type 2 Fuzzy Logics: Illustrative Examples
    DF Using Dempster-Shafer and Possibility Theory: Illustrative Example
    A: Type 1 - Triangular MF-MATLAB Code
    B: Type 2 - Gaussian MF-MATLAB Code
    Fuzzy Inference Calculations - MATLAB Code
    Filtering, Target Tracking and Kinematic Data Fusion
    The Kalman Filter
    The Multi-Sensor Data Fusion and Kalman Filter
    Non-linear Data Fusion Methods
    Data Association in MS Systems
    Information Filtering
    HI Filtering-Based DF
    Optimal Filtering for Data Fusion with Missing Measurements
    Factorisation Filtering and Sensor DF: Illustrative Example
    Decentralised Data Fusion Systems
    Data Fusion Architectures
    Decentralised Estimation and Fusion
    Decentralised Multi-Target Tracking
    Millman's Formulae in Sensor Data Fusion
    SRIF for Data Fusion in Decentralised Network with Four Sensor Nodes: Illustrative Example
    Component Analysis and Data Fusion
    Independent Component Analysis
    An Approach to Image Fusion Using ICA Bases
    Principal Component Analysis
    Discrete-Cosine Transform
    WT: A Brief Theory
    An Approach to Image Fusion Using ICA and Wavelets
    Non-Linear ICA and PCA
    Image Fusion Using MR Singular Value Decomposition
    Image Algebra and Image Fusion
    S. Sethu Selvi
    Image Algebra
    Pixels and Features of an Image
    Inverse Image
    Red, Green and Blue, Grey Images and Histograms
    Image Segmentation
    Noise Processes in an Observed/Acquired Image
    Image Feature Extraction Methods
    Image Transformation and Filtering Approaches
    Image Fusion Mathematics
    Image Fusion Algorithms
    Performance Evaluation
    Multimodal Biometric Systems and Fusion: Illustrative Examples
    Decision Theory and Fusion
    Loss and Utility Functions
    Bayesian DT
    Decision Making with Multiple Information Sources
    Fuzzy Modelling Approach for Decision Analysis/Fusion
    Fuzzy-Evolutive Integral Approach
    Decision Making Based on Voting
    DeF Using FL for Aviation Scenarios
    DeF Strategies
    SA with FL and DeF for Aviation Scenarios: Illustrative Examples
    Wireless Sensor Networks and Multimodal Data Fusion
    Communication Networks and Their Topologies in WSNs
    Sensor/Wireless Sensor Networks
    Wireless Sensor Networks and Architectures
    Sensor Data Fusion in WSN
    Multimodality Sensor Fusion
    Decision Fusion Rules in WSN
    Data Aggregation in WSN
    Hybrid Data and Decision Fusion in WSN
    Optimal Decision Fusion in WSN
    Soft Computing Approaches to Data Fusion
    Artificial Neural Networks
    Radial Basis Function Neural Network
    Recurrent Neural Networks
    FL and Systems as SC Paradigm
    FL in Kalman Filter for Image-Centroid Tracking: A Type of Fusion
    Genetic Algorithms
    SDF Approaches Using SC Methods: Illustrative Examples
    Machine Learning
    Neural-Fuzzy-Genetic Algorithm Fusion
    Image Analysis Using ANFIS: Illustrative Example
    A: Some Algorithms and/or Their Derivations
    B: Other Methods of DF and Fusion Performance Evaluation Metrics
    C:Automatic Data Fusion
    D: Notes and Information on Data Fusion Software Tools
    E: Definitions of Sensor DF in Literature
    F: 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