  In its classical form, the Cardinalized Probability Hypothesis Density (CPHD)
filter does not model the appearance of new targets through spawning, yet there
are applications for which spawning models more appropriately account for
newborn objects when compared to spontaneous birth models. In this paper, we
propose a principled derivation of the CPHD filter with spawning from the
Finite Set Statistics framework. A Gaussian Mixture implementation of the CPHD
filter with spawning is then presented, illustrated with three applicable
spawning models on a simulated scenario involving two parent targets spawning a
total of five objects. Results show that filter implementations with spawn
models provide more accurate results when compared to a birth model
implementation.
