论文标题
算术平均密度融合 - 第二部分：未标记和标记的RFS融合的统一推导
Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
论文作者
论文摘要
作为一种基本信息融合方法，在多传感器多目标跟踪的背景下，最近研究了各种随机有限的滤波器（RFS）滤波器融合的算术平均值（AA）融合。它并不是普通密度-AA融合到RFS分布的直接扩展，而是必须保留融合多目标密度的形式。在这项工作中，我们首先提出了一个统计概念，概率假设密度（PHD）一致性，并解释了如何通过PHD-AA融合可以实现它，并导致更准确，更强大的检测和对当前目标的定位。这既形成理论上的声音和技术意义上有意义的理由，用于执行滤波器间的PHD AA融合/共识，同时保留Fusing RFS滤波器的形式。然后，我们为大多数现有的未标记/标记的RFS过滤器得出并分析适当的AA融合公式，这些滤镜滤波器基于（标记）PHD-AA/一致性。这些推导在理论上是统一的，确切的，不需要近似，并且极大地启用了异质的未标记和标记的RFS密度融合，这在两篇随之而来的伴随论文中被分别证明。
As a fundamental information fusion approach, the arithmetic average (AA) fusion has recently been investigated for various random finite set (RFS) filter fusion in the context of multi-sensor multi-target tracking. It is not a straightforward extension of the ordinary density-AA fusion to the RFS distribution but has to preserve the form of the fusing multi-target density. In this work, we first propose a statistical concept, probability hypothesis density (PHD) consistency, and explain how it can be achieved by the PHD-AA fusion and lead to more accurate and robust detection and localization of the present targets. This forms a both theoretically sound and technically meaningful reason for performing inter-filter PHD AA-fusion/consensus, while preserving the form of the fusing RFS filter. Then, we derive and analyze the proper AA fusion formulations for most existing unlabeled/labeled RFS filters basing on the (labeled) PHD-AA/consistency. These derivations are theoretically unified, exact, need no approximation and greatly enable heterogenous unlabeled and labeled RFS density fusion which is separately demonstrated in two consequent companion papers.
