In this article, we propose a novel approach to distributed joint detection, tracking, and classification (D-JDTC) of multiple targets by means of a multisensor network. The proposed approach relies on labeled multi-Bernoulli (LMB) random finite set modeling of the multisensor state, and consists of two main tasks, that is, local filtering in each individual node and data fusion among multiple nodes. For local filtering, the LMB filter is extended to JDTC by augmenting the target state to incorporate class and mode information. Further, the well-known generalized covariance intersection and recently developed minimum information loss fusion paradigms are exploited for data fusion among sensors. The effectiveness of the resulting algorithm, called D-JDTC-LMB, is assessed via simulation experiments.

中文翻译：

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通过标记多伯努利滤波进行分布式联合检测、跟踪和分类


在本文中，我们提出了一种通过多传感器网络对多个目标进行分布式联合检测、跟踪和分类（D-JDTC）的新方法。该方法依赖于多传感器状态的标记多伯努利（LMB）随机有限集建模，并由两个主要任务组成，即每个单独节点的局部滤波和多个节点之间的数据融合。对于本地过滤，LMB 过滤器通过增强目标状态以合并类别和模式信息来扩展到 JDTC。此外，众所周知的广义协方差交集和最近开发的最小信息损失融合范例被用于传感器之间的数据融合。所得算法（称为 D-JDTC-LMB）的有效性通过模拟实验进行评估。