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The probability hypothesis density (PHD) recursion is a first moment approximation to the multi-target Bayes filter which propagates the posterior intensity of the random finite set of targets in time. The cardinalized PHD (CPHD) recursion is a generalization of the PHD recursion, which jointly propagates the posterior intensity and the posterior cardinality distribution. Using the recently developed closed form solutions to both the PHD and CPHD recursions for linear Gaussian multi-target models, we present a comparative study of their performances
Vo et al. (Sat,) studied this question.