This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.

中文翻译：

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使用反向模拟的多目标轨迹估计

本文提出了一种通用解决方案，用于从一系列多对象（未标记）过滤密度和多对象动态模型中计算轨迹集的多对象后验。重要的是，所提出的解决方案为不明确估计轨迹的多对象滤波器开辟了轨迹估计可能性的途径。在本文中，我们首先推导了一个基于随机有限轨迹集的通用多轨迹反向平滑方程。然后，我们展示了如何使用泊松多伯努利过滤密度的反向模拟对轨迹集进行采样，并开发基于排序分配的易于处理的实现。在模拟研究中评估了由此产生的多轨迹粒子平滑器的性能，