Efficient particle filters for joint tracking and classification

摘要

Target tracking is usually performed using data from sensors such as radar, whilst the target identification task usually relies on information from sensors such as IFF, ESNI or imagery. The differing nature of the data from these sensors has generally led to these two vital tasks being performed separately. However, it is clear that an experienced operator can observe behaviour characteristics of targets and, in combination with knowledge and expectations of target type and likely activity, can more knowledgeably identify the target and robustly predict its track than any automatic process yet defined. Most trackers are designed to follow targets within a wide envelope of trajectories and are not designed to derive behaviour characteristics or include them as part of their output. Thus, there is potential scope for both applying target type knowledge to improve the reliability of the tracking process, and to derive behavioural characteristics which may enhance knowledge about target identity and/or activity. In this paper we introduce a Bayesian framework for joint tracking and identification and give a robust and computationally efficient particle filter based algorithm for numerical implementation of the resulting recursions. Simulation results illustrating algorithm performance are presented.