Information Fusion based decision support via Hidden Markov Models and time series anomaly detection

An information fusion (IF) based decision support tool (DST) is presented to aid the identification of a target, from a large set of candidates, carrying out a pattern of activity which could be comprised of a wide variety of possible sub-activities and chronologies of events. The overall activity can only be defined in terms of its impact and in some cases detectable signatures of sub-activities. hidden Markov models (HMMs) and time series anomaly detection methods process multi-modal sensor data which are then integrated by a novel, efficient Bayesian IF algorithm to provide a probability that each candidate under observation is carrying out the target activity. The DST has been developed to prototype status by implementing this framework using commercial off the shelf (COTS) software. The DST allows the decision maker to rapidly access current and historical situational awareness pictures quantifying the progress of the overall search. A range of geospatial visualization and data interrogation features available to the decision maker are described and their performance is qualitatively evaluated. Finally, planned future developments are outlined.