This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli \textit{random finite set} (RFS) uniquely characterized by existence, classification, class-conditioned mode and class\&mode-conditioned state probability distributions. By designing suitable centralized and distributed rules for fusing information on target existence, class, mode and state from different sensor nodes, novel \textit{centralized} and \textit{distributed} JDTC \textit{Bernoulli filters} (C-JDTC-BF and D-JDTC-BF), are proposed. The performance of the proposed JDTC-BF approach is evaluated by means of simulation experiments.

中文翻译：

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基于伯努利滤波器的多传感器联合目标检测、跟踪和分类

本文侧重于通过多传感器融合对目标的 \textit{联合检测、跟踪和分类}（JDTC）。目标可以存在或不存在，可以属于不同的类，并且根据其类可以根据不同的运动模式表现。因此，它被建模为一个适当扩展的伯努利\textit{随机有限集}（RFS），其唯一特征是存在、分类、类条件模式和类\&模式条件状态概率分布。通过设计合适的集中式和分布式规则来融合来自不同传感器节点的目标存在、类别、模式和状态的信息，新颖的 \textit{centralized} 和 \textit{distributed} JDTC \textit{Bernoulli 过滤器}（C-JDTC-BF 和D-JDTC-BF)，建议。