Advancements in situation assessment sensor management

摘要

In last year's conference we demonstrated new results using a foundational, joint control-theoretic approach to situation assessment (SA) and SA sensor management that is based on a "dynamic situational significance map", the maximization of the expected number of targets of tactical interest, and approximate multitarget filters (specifically, first-order multitarget moment filters and multi-hypothesis correlator (MHC) engines). This year we report on the following new developments and extensions: (1) a tactical significance function based on the fusion of different ambiguous attributes from several different sources; (2) a Bayes' belief network formulation for multi-target tracking and information fusion; and (3) a recursive closed form expression for the posterior expected number of targets of interests (PENTIs) for ANY number of sources. Results of testing this sensor management algorithm with significance maps defined in terms of targets/attributes interrelationships using simplified battlefield situations demonstrate that these new advancements allow for a better SA, and a more efficient SA sensor management.