The paper addresses the problem of distributed multi-target tracking (MTT) in a network of sensors having different fields of view (FoVs). Probability hypothesis density (PHD) filters are running locally in every sensor for MTT. The weighted arithmetic average (WAA) fusion rule is employed to fuse the multiple local PHD densities due to its computational efficiency. First, we provide a theoretical analysis showing that the standard WAA fusion among sensors with different FoVs is unsuitable from the perspective of the principle of minimum discrimination information (PMDI). In fact, the information inconsistency among sensors due to the different FoVs unavoidably leads to an underestimation of the fused PHD. Then, motivated by the analysis, we devise two novel approaches to address the different FoV issue. The first approach accounts for the case where the sensor FoVs are known and time-invariant. The second one deals with the more complicated case where the actual sensor FoVs can be cropped due to unknown line of sight obstructions or sensors can obtain the information outside their current FoVs because of platform movement or information feedback. The essence of the proposed approaches is to perform the WAA in a more robust way by employing a set of state-dependent fusion weights which are computed online. The Gaussian mixture implementations of the proposed methods are also presented. Various simulation experiments, including a challenging tracking scenario involving six sensors with different FoVs and also random line of sight obstructions, are designed to demonstrate the efficacy of the proposed fusion approaches.

中文翻译：

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具有不同传感器视场的PHD滤波器的分布式多传感器融合

该论文解决了具有不同视场 (FoV) 的传感器网络中的分布式多目标跟踪 (MTT) 问题。概率假设密度 (PHD) 过滤器在 MTT 的每个传感器中本地运行。由于其计算效率，采用加权算术平均（WAA）融合规则来融合多个局部PHD密度。首先，我们提供了一个理论分析，表明从最小判别信息（PMDI）原则的角度来看，具有不同 FoV 的传感器之间的标准 WAA 融合是不合适的。事实上，由于不同的 FoV 导致传感器之间的信息不一致不可避免地导致对融合 PHD 的低估。然后，在分析的推动下，我们设计了两种新颖的方法来解决不同的 FoV 问题。第一种方法考虑了传感器 FoV 已知且不随时间变化的情况。第二个处理更复杂的情况，即由于未知的视线障碍物可以裁剪实际传感器 FoV，或者由于平台移动或信息反馈，传感器可以获得其当前 FoV 之外的信息。所提出方法的本质是通过采用一组在线计算的状态相关融合权重，以更稳健的方式执行 WAA。还介绍了所提出方法的高斯混合实现。各种模拟实验，包括一个具有挑战性的跟踪场景，涉及六个具有不同 FoV 的传感器以及随机视线障碍物，旨在证明所提出的融合方法的有效性。