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23 May 2013Tracking, identification, and classification with random finite sets
This paper considers the problem of joint multiple target tracking, identification, and classification. Standard
approaches tend to treat the tasks of data association, estimation, track management and classification as
separate problems. This paper outlines how it is possible to formulate a unified a Bayesian recursion for joint
tracking, identification and classification. The formulation is based on the theory of random finite sets or finite set
statistics, and specifically labeled random finite sets, which results in a propagation of a multi-target posterior
which contains not only target information but all available track information. Implementations are briefly
discussed. Where appropriate for particular applications this method can be considered Bayes optimal.
Ba Tuong Vo andBa Ngu Vo
"Tracking, identification, and classification with random finite sets", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450D (23 May 2013); https://doi.org/10.1117/12.2015370
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Ba Tuong Vo, Ba Ngu Vo, "Tracking, identification, and classification with random finite sets," Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450D (23 May 2013); https://doi.org/10.1117/12.2015370