In this article, we propose an efficient random finite set (RFS)-based algorithm for multiobject tracking, in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new labeled Bernoulli component is created, and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.

中文翻译：

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一种高效的多目标跟踪标记/无标记随机有限集算法


在本文中，我们提出了一种基于随机有限集 (RFS) 的高效多目标跟踪算法，其中目标状态通过标记多伯努利 (LMB) RFS 和泊松 RFS 的组合进行建模。该算法中计算要求较低的泊松部分用于跟踪不太可能存在的潜在物体。仅当表征对象存在合理性的量高于阈值时，才会创建新的标记伯努利分量，并且通过算法中更准确但计算要求更高的 LMB 部分来跟踪对象。相反，如果相应的存在概率低于另一个阈值，则标记的伯努利分量将被转移回泊松 RFS。与基于多伯努利和泊松 RFS 的现有混合算法相反，所提出的方法有利于轨道连续性并实现降低复杂性的功能。仿真结果表明，与性能相当的其他基于 RFS 的算法相比，复杂性大大降低。