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.
