Bayes-Optimal Set-Valued Tracking of Single Point Targets

Abstract

Conventional single-point-target tracking algorithms are recursive point estimators with point-measurement input data. In less well-known approaches, the tracking algorithm is a recursive set estimator with point-measurement or set-measurement input data. This paper provides a very general, systematic, and theoretically rigorous foundation for Bayes-optimal single-target trackers of the latter kind; as well as specific trackers arising from that foundation. This foundation is intuitive and conceptually simple and, in particular, requires no measure-theoretic complexities.

Keywords

• Target tracking
• random closed set
• finite-set statistics
• recursive Bayes filter
• generalized measurement
• generalized likelihood function