This paper presents the distributed Poisson multi-Bernoulli (PMB) filter based on the generalised covariance intersection (GCI) fusion rule for distributed multi-object filtering. Since the exact GCI fusion of two PMB densities is intractable, we derive a principled approximation. Specifically, we approximate the power of a PMB density as an unnormalised PMB density, which corresponds to an upper bound of the PMB density. Then, the GCI fusion rule corresponds to the normalised product of two unnormalised PMB densities. We show that the result is a Poisson multi-Bernoulli mixture (PMBM), which can be expressed in closed form. Future prediction and update steps in each filter preserve the PMBM form, which can be projected back to a PMB density before the next fusion step. Experimental results show the benefits of this approach compared to other distributed multi-object filters.

翻译：本文提出了一种基于广义协方差交集融合规则的分布式泊松多伯努利滤波器，用于分布式多目标跟踪。由于两个PMB密度的精确GCI融合难以处理，我们推导了一种原理性近似方法。具体而言，我们将PMB密度的幂次近似为一个未归一化的PMB密度，该密度对应于PMB密度的上界。随后，GCI融合规则对应于两个未归一化PMB密度的归一化乘积。我们证明该结果是一个泊松多伯努利混合密度，其具有闭式表达式。各滤波器中的预测与更新步骤均保持PMBM形式，该形式可在下一次融合步骤前投影回PMB密度。实验结果表明，相较于其他分布式多目标滤波器，本方法具有显著优势。