Efficient particle filters for joint tracking and classification

Neil J. Gordon; Simon Maskell; Thiagalingam Kirubarajan

doi:10.1117/12.478524

7 August 2002 Efficient particle filters for joint tracking and classification

Neil J. Gordon, Simon Maskell, Thiagalingam Kirubarajan

Proceedings Volume 4728, Signal and Data Processing of Small Targets 2002; (2002) https://doi.org/10.1117/12.478524
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

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, ESM 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 behavior 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 behavior 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 behavioral 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.

Citation Download Citation

Neil J. Gordon, Simon Maskell, and Thiagalingam Kirubarajan "Efficient particle filters for joint tracking and classification", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); https://doi.org/10.1117/12.478524

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