标题： 广义标记多伯努利滤波器与多目标关联模型 显示英文标题

标题： Generalized Labeled Multi-Bernoulli Filters and Multitarget-Correlation Models

摘要： 广义标记多伯努利（GLMB）滤波器是一种理论上严格的贝叶斯最优多目标跟踪算法，并且具有计算上可行的实现方式，它基于带标签的随机有限集（LRFS）理论。它假设多目标群体可以通过GLMB多目标概率密度函数（p.d.f.）来近似表示，这些函数由关于当前目标状态的加权假设组成。GLMB p.d.f.的一个特例——LMB p.d.f.——假设目标在统计上是独立的。本文证明了以下三点：a) 在隐含假设“简单带标签相关”（SLC）模型的前提下，GLMB p.d.f.可以被解释为LMB p.d.f.到统计相关的多目标群体的直接推广；b) GLMB滤波器可以重新表述为SLC-GLMB滤波器；c) SLC模型似乎主要适用于由少量紧密排列的目标组成的目标簇。 显示英文摘要

摘要： The generalized labeled multi-Bernoulli (GLMB) filter is a theoretically rigorous Bayes-optimal multitarget tracking algorithm with computationally tractable implementations, based on labeled random finite set (LRFS) theory. It presumes that multitarget populations can be approximated using GLMB multitarget probability density functions (p.d.f.'s), which consist of weighted hypotheses regarding the current target-states. A special case of the GLMB p.d.f.-the LMB p.d.f.-presumes that the targets are statistically independent. This paper demonstrates that a) GLMB p.d.f.'s can be interpreted as straightforward generalizations of LMB p.d.f.'s to statistically correlated target populations, given an implicit presumption of "simple labeled correlation" (SLC) models of multitarget correlation; b) the GLMB filter can be reformulated as a SLC-GLMB filter; and c) SLC models seem primarily appropriate for target clusters consisting of small numbers of closely-spaced targets.