High-Level Data Fusion

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.

Data fusion technologies produce incredibly powerful situation assessment tools with widespread military and non-military applications. This cutting-edge book arms the fast-growing field of fusion system developers and researchers with the essential tools and latest techniques they need to develop next-generation fusion services, select algorithms and software, simulate performance, and evaluate systems with never-before effectiveness. The book explores techniques for applications such as target classification/identification and unit aggregation, assessing their suitability and capabilities in each case. Moreover, it spotlights emerging technologies like particle filtering, clustering, net-centricity, agent formalism, and distributed fusion.
Market
Data fusion system developers and engineers, government researchers, and military professionals and graduate students interested in data fusion.

Subrata Das is the chief scientist at Charles River Analytics, Inc. in Cambridge, MA, where he leads projects in data fusion, decision-making under uncertainty, intelligent agents, computational artificial intelligence, and machine learning. 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.