论文标题
使用标记的多坐密度的GCI融合的方法
An Approach for GCI Fusion With Labeled Multitarget Densities
论文作者
论文摘要
本文讨论了标记为随机有限集的广义协方差交叉点（GCI）融合方法。我们提出了一个联合标签空间，以支持融合标记的随机有限集，以表示不同试剂之间的标签关联，从而避免了标签GCI融合算法的标签一致性条件。具体而言，我们通过每个代理的所有标签空间的直接乘积设计关节标签空间。然后，我们应用GCI融合方法来获得联合标记的多目标密度。然后将关节标记的RFS边缘化成一个通用标记的RF，提供每个目标由具有唯一标签的单个Bernoulli组件表示。展示了来自不同试剂的LMB RFS的联合标记的GCI（JL-GCI）。我们还提出了简化的JL-GCI方法，因为假设目标在方案中是完善的。模拟结果列出了标签不一致和在挑战跟踪方案中出色的性能的有效性。
This paper addresses the Generalized Covariance Intersection (GCI) fusion method for labeled random finite sets. We propose a joint label space for the support of fused labeled random finite sets to represent the label association between different agents, avoiding the label consistency condition for the label-wise GCI fusion algorithm. Specifically, we devise the joint label space by the direct product of all label spaces for each agent. Then we apply the GCI fusion method to obtain the joint labeled multi-target density. The joint labeled RFS is then marginalized into a general labeled RFS, providing that each target is represented by a single Bernoulli component with a unique label. The joint labeled GCI (JL-GCI) for fusing LMB RFSs from different agents is demonstrated. We also propose the simplified JL-GCI method given the assumption that targets are well-separated in the scenario. The simulation result presents the effectiveness of label inconsistency and excellent performance in challenging tracking scenarios.
