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Artech House USA
Mathematical Techniques in Multisensor Data Fusion, Second Edition

Mathematical Techniques in Multisensor Data Fusion, Second Edition

Copyright: 2004
Pages: 466
ISBN: 9781580533355

Artech House is pleased to offer you this title in a special In-Print-Forever® ( IPF® ) hardbound edition. This book is not available from inventory but can be printed at your request and delivered within 2-4 weeks of receipt of order. Please note that because IPF® books are printed on demand, returns cannot be accepted.


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Since the publication of the first edition of this groundbreaking book, advances in algorithms, logic, and software tools have transformed the field of data fusion. The latest edition covers these areas as well as smart agents, human computer interaction, cognitive aides to analysis, and data system fusion control. Besides aiding you in selecting the appropriate algorithm for implementing a data fusion system, this book guides you through the process of determining the trade-offs among competing data fusion algorithms, selecting commercial off the shelf (COTS) tools, and understanding when data fusion improves systems processing. Completely new chapters in this second edition explain data fusion system control, DARPA's recently developed TRIP model, and the latest applications of data fusion in data warehousing and medical equipment, as well as defense systems.
Introduction to Multisensor Data Fusion -Introduction, Fusion Applications, Sensors and Sensor Data, The Inference Hierarchy, A Data Fusion Model, Benefits of Data Fusion, Architectural Concepts and Issues. Limitation of Data Fusion, References; Taxonomy of Algorithms and JDL Model - Taxonomy Overview, Positional Fusion Algorithms, Identity Fusion Algorithms, Ancillary Support Algorithms, References; Level 1: Data Association and Correlation - Process Model for Correlation, Hypothesis Generation, Hypothesis Evaluation, Hypothesis Selection Techniques, References; Level 1: Kinematic and Attribute Estimation -Introduction, Overview of Estimation Techniques, Batch Estimation, Sequential Estimation, Covariance Error Estimation, References; Level 1: Identity Declaration - Identity Declaration and Pattern Recognition, Feature Extraction, Parametric Templates, Cluster Analysis Techniques, Adaptive Neural Networks, Physical Models, Knowledge-Based Methods, Hybrid Methods, References; Level 1: Decision-Level Identity Fusion -Introduction, Classical Inference, Bayesian Inference, Dempster-Shafer 's Method, Generalized Evidence Processing Theory, Heuristic Methods, Implementation and Tradeoffs, References; Levels 2 and 3: Knowledge-Based Approaches -Introduction to Artificial Intelligence, Overview of Expert Systems, Bayesian Belief Nets, Intelligent Agent Systems, Implementation of Expert Systems, Logical Templating Techniques, References; Level 4: Process Monitoring and Optimization -Introduction, Extending the Concept of Level 4 Processing, Techniques for Level 4 Processing, Auction-based Methods, and References; Level 5: Human Computer Interaction -Introduction, Cognitive Aspects of Situation Assessment, Individual Differences in Information Processing, Enabling HCI Technologies, Computer-Aided Situation Assessment, An SBIR Experiment, References; Implementing Data Fusion Systems -Introduction, Requirements Derivation, Sensor Selection and Evaluation, Function Allocation and Decomposition, Architecture Definition, Algorithm Selection, Data Base Definition, HCI Design, Test and Evaluation, References; Emerging Applications -Introduction, Military Applications; Emerging Nonmilitary Applications; COTS Software Survey, Perspectives and Comments, References; TRIP Model - Background,Introduction, TRIP Model, Process Control, Functional Analyses using the TRIP Model, Application of the TRIP Model to the Information Production Process, Summary, References; Automated Information Management -Introduction, Initial Automated Information Manager, Automated Targeting Data Fusion: Structure and Flow, References; Index;
  • David L. Hall David L. Hall is a professor in the College of Information Sciences and Technology(IST) at The Pennsylvania State University. He is also the author of Mathematical Techniques in Multisensor Data Fusion, Second Edition (Artech House, 2004). Dr. Hall has been named an IEEE fellow for his contributions to data fusion and he is a past recipient of the DoD Joe Mignona National Data Fusion Award. He earned his Ph.D. in Astronomy at The Pennsylvania State University.
  • Sonya A.H. McMullen Sonya A. H. McMullen is a Captain with the US Air Force. She earned an M.S. in aerospace engineering at Embry-Riddle Aeronautic University.
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