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Modeling and Detection of Evolving Threats Using Random Finite Set Statistics

Many threats in the form of human actions (terrorist attacks, military actions, etc.) can be modeled by someone with relevant expert knowledge. A model would be a hypothesis or guess as to how a threat would develop and what kind of observable evidence it would produce along the way. We present a method of stochastically modeling these types of processes using Hidden Markov Models (HMMs). We then present a detection scheme using a Bernoulli Filter - an increasingly popular application of random finite set statistics