标题： CPHD滤波器的产卵模型 显示英文标题

标题： Spawning Models for the CPHD Filter

摘要： 在经典形式中，基数化概率假设密度（CPHD）滤波器不通过萌生来建模新目标的出现，但在某些应用中，与自发出生模型相比，萌生模型更能恰当地解释新生对象。 本文提出了一种基于有限集统计框架的CPHD滤波器与萌生相结合的原理性推导方法。 随后介绍了CPHD滤波器与萌生相结合的高斯混合实现，并通过三个适用于模拟场景中的适用萌生模型进行了说明，该场景涉及两个母目标共萌生出五个对象。 结果显示，与出生模型实现相比，具有萌生模型的滤波器实现提供了更准确的结果。 显示英文摘要

摘要： 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.