A Bayesian Framework for Joint Target Tracking, Classification, and Intent Inference

Abstract

Intent inference has attracted considerable interest for achieving situation awareness in the high-level information fusion community. Different from traditional tracking-then-inference methods for intent inference, a novel scheme for joint target tracking, classification, and intent inference (JTCI) is developed based on the Bayesian framework. The proposed JTCI scheme exploits the dependence of target state on target class and intent by defining intent and class dependent dynamic model sets. Then, the joint target state, intent, and class density are obtained recursively under the assumption, and the kinematic and attribute measurement processes are conditional independent. Finally, simulations about tracking in the air surveillance system are utilized to demonstrate the superiority of the proposed JTCI to the state-of-the-art JTC.