This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion. After the update step, the posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a Kullback-Leibler divergence minimisation on an augmented space is performed. The developed filters are computationally lighter alternatives to the trajectory PMBM filters, which provide the closed-form recursion for sets of trajectories with Poisson birth model, and are shown to outperform previous multi-target tracking algorithms.

中文翻译：

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轨迹泊松多伯努利滤波器

本文提出了用于多目标跟踪的两种轨迹泊松多伯努利 (TPMB) 滤波器：一种用于估计每个时间步的活动轨迹集，另一种用于估计所有轨迹的集合，包括活轨迹和死轨迹，在每个时间步时间步长。过滤器基于通过过滤递归在相应的一组轨迹上传播泊松多伯努利 (PMB) 密度。在更新步骤之后，后验是 PMB 混合 (PMBM)，因此，为了获得 PMB 密度，在增强空间上执行 Kullback-Leibler 散度最小化。开发的滤波器是轨迹 PMBM 滤波器的计算量更轻的替代品，它为具有泊松出生模型的轨迹集提供封闭形式的递归，