Consensus-Based Labeled Multi-Bernoulli Filter for Multitarget Tracking in Distributed Sensor Network
提出一种新的共识标记多伯努利滤波器，解决分布式传感器网络中节点视野不同导致的多目标跟踪问题，通过扩展标签空间映射和自适应权重调整实现鲁棒融合。
This article introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multitarget tracking (MTT) in a distributed sensor network (DSN), whose sensor nodes have limited and different fields of view (FoVs). Although consensus-based algorithms are effective for distributed fusion and MTT, it may be problematic when distributed sensor nodes have different FoVs. To deal with this issue, the proposed method constructs an extended label space mapping to overcome the "label space mismatching" phenomenon; after that, the model of the undetected multitargets is established so that the tracks can be initialized outside the FoV of local sensors; finally and most important, weight selection and evolution mechanism are proposed such that the fusion weights are automatically tuned for each track at each time step and consensus step. The efficiency and robustness of the proposed algorithm are demonstrated in a distributed MTT scenario via numerical simulations.