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In its classical form, the Cardinalized Probability Hypothesis Density (CPHD)filter does not model the appearance of new targets through spawning, yet thereare applications for which spawning models more appropriately account fornewborn objects when compared to spontaneous birth models. In this paper, wepropose a principled derivation of the CPHD filter with spawning from theFinite Set Statistics framework. A Gaussian Mixture implementation of the CPHDfilter with spawning is then presented, illustrated with three applicablespawning models on a simulated scenario involving two parent targets spawning atotal of five objects. Results show that filter implementations with spawnmodels provide more accurate results when compared to a birth modelimplementation.

Spawning Models for the CPHD Filter