Multisensor Data Fusion (eBook, PDF)

From Algorithms and Architectural Design to Applications
Redaktion: Fourati, Hassen

• Format: PDF

Produktbeschreibung

This book covers the theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from data fusion researchers and academicians, the text discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies. It presents state-of-the-art advances in the design of data fusion algorithms and architectures, as well as showcases applications in fields such as medicine, transportation's traffic, defense, and navigation.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 663
• Erscheinungstermin: 19. Dezember 2017
• Englisch
• ISBN-13: 9781482263756
• Artikelnr.: 43975638

Autorenporträt

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.

Inhaltsangabe

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.