Distributed Joint Detection, Tracking, and Classification via Labeled Multi-Bernoulli Filtering
提出一种基于标签多伯努利滤波的分布式多传感器联合检测、跟踪与分类方法，通过局部滤波与数据融合实现多目标状态估计，适用于传感器网络场景。
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.