Multisensor Data Fusion
From Algorithms and Architectural Design to Applications
Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book:
- Presents state-of-the-art advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors
- Describes new materials and achievements in optimal fusion and multisensor filters
- Discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies
- Explores the topology, communication structure, computational resources, fusion level, goals, and optimization of multisensor data fusion system architectures
- Showcases applications of multisensor data fusion in fields such as medicine, transportation's traffic, defense, and navigation
Multisensor Data Fusion: From Algorithms and Architectural Design to Applications is a robust collection of modern multisensor data fusion methodologies. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution.
Table of Contents
Challenges in Information Fusion Technology Capabilities for Modern Intelligence and Security Problems. Multisensor Data Fusion: A Data-Centric Review of the State of the Art and Overview of Emerging Trends. Information Fusion: Theory at Work. JDL Model (III) Updates for an Information Management Enterprise. Elements of Random Set Information Fusion. Optimal Fusion for Dynamic Systems with Process Noise. A Fuzzy Multicriteria Approach for Data Fusion. Distributed Detection and Data Fusion with Heterogeneous Sensors. Fusion Systems Evaluation: An Information Quality Perspective. Sensor Failure Robust Fusion. Treatment of Dependent Information in Multisensor Kalman Filtering and Data Fusion. Cubature Information Filters: Theory and Applications to Multisensor Fusion. Estimation Fusion for Linear Equality Constrained Systems. Nonlinear Information Fusion Algorithm of an Asynchronous Multisensor Based on the Cubature Kalman Filter. The Analytic Implementation of the Multisensor Probability Hypothesis Density Filter. Information Fusion Estimation for Multisensor Multirate Systems with Multiplicative Noises. Optimal Distributed Kalman Filtering Fusion with Singular Covariances of Filtering Errors and Measurement Noises. Accumulated State Densities and Their Applications in Object Tracking. Belief Function-Based Multisensor Multitarget Classification Solution. Decision Fusion in Cognitive Wireless Sensor Networks. Dynamics of Consensus Formation among Agent Opinions. Decentralized Bayesian Fusion in Networks with Non-Gaussian Uncertainties. Attack-Resilient Sensor Fusion for CPS. Multisensor Data Fusion for Water Quality Evaluation Using Dempster–Shafer Evidence Theory. A Granular Sensor-Fusion Method for Regenerative Life Support Systems. Evaluating Image Fusion Performance: From Metrics to Cognitive Assessment. A Review of Feature and Data Fusion with Medical Images. Multisensor Data Fusion: Architecture Design and Application in Physical Activity Assessment. Data Fusion for Attitude Estimation of a Projectile: From Theory to In-Flight Demonstration. Data Fusion for Telemonitoring: Application to Health and Autonomy. Sensor Data Fusion for Automotive Systems. Data Fusion in Intelligent Traffic and Transportation Engineering: Recent Advances and Challenges. Application of Multisensor Data Fusion for Traffic Congestion Analysis. Consensus-Based Decentralized Extended Kalman Filter for State Estimation of Large-Scale Freeway Networks.
Hassen Fourati is currently an associate professor in the Electrical Engineering and Computer Science Department at the University Grenoble Alpes, Grenoble, France, and a member of the Networked Controlled Systems Team (NeCS), affiliated with the Automatic Control Department of the Laboratoire Grenoble Images Parole Signal Automatique (GIPSA-LAB) and the Institut National de Recherche en Informatique et en Automatique (INRIA). He holds a B.Eng in electrical engineering from the National Engineering School of Sfax, Tunisia; master’s in automated systems and control from the University of Claude Bernard, Lyon, France; and Ph.D in automatic control from the University of Strasbourg, France. His research interests include nonlinear filtering, estimation, and multisensor fusion with applications in navigation, inertial and magnetic sensors, robotics, and traffic management. He has published several research journal articles, papers in international conferences, and book chapters.