The growing need for accurate and reliable tracking systems has driven significant progress in sensor fusion and object tracking techniques. In this paper, we design two variational Bayesian trackers that effectively track multiple targets in cluttered environments within a sensor network. We first present a centralised sensor fusion scheme, which involves transmitting sensor data to a fusion center. Then, we develop a distributed version leveraging the average consensus algorithm, which is theoretically equivalent to the centralised sensor fusion tracker and requires only local message passing with neighbouring sensors. In addition, we empirically verify that our proposed distributed variational tracker performs on par with the centralised version with equal tracking accuracy. Simulation results show that our distributed multi-target tracker outperforms the suboptimal distributed sensor fusion strategy that fuses each sensor's posterior based on arithmetic sensor fusion and an average consensus strategy.

翻译：随着对精确可靠跟踪系统的需求日益增长，传感器融合与目标跟踪技术取得了显著进展。本文针对传感器网络中的杂波环境，设计了两种有效跟踪多个目标的变分贝叶斯跟踪器。首先提出一种集中式传感器融合方案，该方案将传感器数据传输至融合中心。随后，基于平均共识算法开发了分布式版本，该版本在理论上等效于集中式传感器融合跟踪器，且仅需与相邻传感器进行局部消息传递。此外，我们通过实验验证了所提出的分布式变分跟踪器在跟踪精度上与集中式版本性能相当。仿真结果表明，与基于算术传感器融合及平均共识策略的次优分布式传感器融合方法相比，本文提出的分布式多目标跟踪器具有更优性能。