This paper addresses the density based multi-sensor cooperative fusion using random finite set (RFS) type multi-object densities (MODs). Existing fusion methods use scalar weights to characterize the relative information confidence among the local MODs, and in this way the portion of contribution of each local MOD to the fused global MOD can be tuned via adjusting these weights. Our analysis shows that the fusion mechanism of using a scalar coefficient can be oversimplified for practical scenarios, as the information confidence of an MOD is complex and usually space-varying due to the imperfection of sensor ability and the various impacts from surveillance environment. Consequently, severe fusion performance degradation can be observed when these scalar weights fail to reflect the actual situation. We make two contributions towards addressing this problem. Firstly, we propose a novel heterogeneous fusion method to perform the information averaging among local RFS MODs. By factorizing each local MODs into a number of smaller size sub-MODs, it can transform the original complicated fusion problem into a much easier parallelizable multi-cluster fusion problem. Secondly, as the proposed fusion strategy is a general procedure without any particular model assumptions, we further derive the detailed heterogeneous fusion equations, with centralized network architecture, for both the probability hypothesis density (PHD) filter and the multi-Bernoulli (MB) filter. The Gaussian mixture implementations of the proposed fusion algorithms are also presented. Various numerical experiments are designed to demonstrate the efficacy of the proposed fusion methods.

翻译：本文用随机限量集集( RFS) 类型多对象密度(MOD) 处理基于密度的多传感器或合作聚合。 现有的聚合方法使用卡路里重量来说明当地MOD之间的相对信息信任度, 从而可以通过调整这些重量来调整每个本地MOD对接合全球MOD的贡献部分。 我们的分析表明, 使用一个卡路里系数的聚合机制对于实际情景来说可能过于简单化, 因为一个MOD的信息信心复杂, 通常由于传感器能力的不完善和监视环境的各种数值实验效果而使空间变化不定。 因此, 当这些卡路里重量不能反映当地MOD之间的相对信息信任度时, 可以观察到严重的聚变性性退化。 我们为解决这一问题做出两种贡献。 首先, 我们提出一种新的混杂方法, 来进行当地RFS MOD 平均的信息。 通过将每个本地显示的混合系数纳入一个较小规模的子MOD, 它可以将最初复杂的混合问题转化成一个相当容易的模型化的多团级比值, 并且提出一个我们提出的多级化的模型的模型的模型化模型化的计算, 也是一个我们提出的多级化的模型的模型的模型的模型的模型的模型的计算 。 。 。 。