Joint class identification and target classification using multiple HMMs

Abstract

Target classification has received significant attention in the tracking literature. Algorithms for joint tracking and classification that are capable of improving tracking performance by exploiting the interdependency between target class and target kinematic behavior have already been proposed. In these works, target identification relies on the a priori information about target classes, but, in practice, the prior class information may not always be available or not accurate. This motivates the design of a new estimation method that can jointly build target classes and classify targets even when a priori information is not available. Based on the generic expectation-maximization framework, a novel joint multitarget class estimation and target identification algorithm that requires only target feature measurements is proposed in this paper to achieve this goal. In this approach, multitarget classes are characterized by multiple hidden Markov models. Besides theoretical derivations, simulations are presented to verify the effectiveness of the proposed algorithm.