Master cutting-edge Level 2 fusion techniques that help you develop powerful situation assessment services with eye-popping capabilities and performance with this trail-blazing resource. The book explores object and situation fusion processes with an appropriate handling of uncertainties, and applies cutting-edge artificial intelligence and emerging technologies like particle filtering, spatiotemporal clustering, net-centricity, agent formalism, and distributed fusion together with essential Level 1 techniques and Level 1/2 interactions. Moreover, it includes all the tools you need to design high-level fusion services, select algorithms and software, simulate performance, and evaluate systems with never-before effectiveness. The book explains the Bayesian, fuzzy, and belief function formalisms of data fusion and a review of Level 1 techniques, including essential target tracking methods. Further, it covers Level 2 fusion methods for applications such as target classification and identification, unit aggregation and ambush detection, threat assessment, and relationships among entities and events, and assessing their suitability and capabilities in each case. The book's detailed discussion of Level 1/2 interactions emphasizes particle filtering techniques as unifying methods for both filtering under Level 1 fusion and inferencing in models for Level 2 fusion. The book also describes various temporal modeling techniques including dynamic Bayesian networks and hidden Markov models, distributed fusion for emerging network centric warfare environments, and the adaptation of fusion processes via machine learning techniques. Packed with real-world examples at every step, this peerless volume serves as an invaluable reference for your research and development of next-generation data fusion tools and services.
Background and Concepts. Approaches to Handling Uncertainty. Target Tracking. Target Classification and Identification. Unit Aggregation. Model-Based Situation Abstraction. Modeling of Time for Situation Assessment. Performance Enhancement and Evaluation. Decision Support. Distributed Situation Assessment. Learning of Fusion Models.; To view complete TOC:; Click Google Preview button under book title above, then click on Contents tab.;
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Subrata Das
Subrata Das is the chief scientist at Charles River Analytics, Inc. in Cambridge, MA, where he leads projects in data fusion fusion, decision-making under uncertainty, intelligent agents, computational artificial intelligence, and machine learning. Previously, he held research positions at Imperial College and Queen Mary and Westfield College, both part of the University of London. Dr. Das is the author of numerous journal and conference articles, author/co-author of two other books, and an editorial board member of the journal Information Fusion. Additionally, he has been a contributor, committee member, and lecturer at each of the last five International Conferences on Information Fusion. Dr. Das received his Ph.D. in computer science from Heriot-Watt University, Scotland and his M.Tech degree from the Indian Statistical Institute, Kolkata, India.