标题： 基于广义协方差交叉的分布式泊松多伯努利滤波 显示英文标题

标题： Distributed Poisson multi-Bernoulli filtering via generalised covariance intersection

摘要： 本文提出了基于广义协方差交叉（GCI）融合规则的分布式泊松多伯努利（PMB）滤波器，用于分布式多目标过滤。 由于两个PMB密度的精确GCI融合是难以处理的，我们推导了一个合理的近似方法。 具体来说，我们将PMB密度的幂次近似为一个非归一化的PMB密度，这对应于PMB密度的一个上界。 然后，GCI融合规则对应于两个非归一化PMB密度的归一化乘积。 我们证明结果是一个泊松多伯努利混合（PMBM），可以用闭合形式表示。 每个滤波器中的未来预测和更新步骤保持PMBM形式，可以在下一次融合步骤之前投影回一个PMB密度。 实验结果表明，与其它分布式多目标滤波器相比，这种方法具有优势。 显示英文摘要

摘要： 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.