Adaptive Target Birth Intensity for PHD and CPHD Filters
Adaptive Target Birth Intensity for PHD and CPHD Filters
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DOI：
10.1109/taes.2012.6178085
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发表时间：
2012-04-01
影响因子：
4.4
通讯作者：
Vo, Ba-Tuong
中科院分区：
文献类型：
--
作者：
Ristic, B.;Clark, D.;Vo, Ba-Tuong
概率假设密度（PHD）和基数化PHD（CPHD）滤波器的标准公式假设目标出生强度是先验已知的。在目标可以出现在任何地方的监视体积的情况下，这显然是低效的，因为目标的出生强度需要覆盖整个状态空间。本文提出了一种新的扩展的PHD和CPHD过滤器，区分持久性和新生儿的目标。该扩展使我们能够使用接收到的测量值在每次扫描时自适应地设计目标出生强度。顺序蒙特-卡罗（SMC）的实现所产生的PHD和CPHD滤波器和它们的性能进行了数值研究。提出的测量驱动的出生强度提高了目标数量及其空间分布的估计准确度。
The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters assumes that the target birth intensity is known a priori. In situations where the targets can appear anywhere in the surveillance volume this is clearly inefficient, since the target birth intensity needs to cover the entire state space.This paper presents a new extension of the PHD and CPHD filters, which distinguishes between the persistent and the newborn targets. This extension enables us to adaptively design the target birth intensity at each scan using the received measurements. Sequential Monte-Carlo (SMC) implementations of the resulting PHD and CPHD filters are presented and their performance studied numerically. The proposed measurement-driven birth intensity improves the estimation accuracy of both the number of targets and their spatial distribution.