边缘多伯努利滤波器：MHT、JIPDA和基于关联的MeMBer的RFS推导
Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
AI总结：
本文通过随机有限集推导全贝叶斯RFS滤波器，揭示数据关联隐式存在，并通过近似关联分布得到与JIPDA和MeMBer相关的两种算法，在复杂环境下提升性能。
摘要：
随机有限集（RFS）的最新发展产生了多种避免数据关联的跟踪方法。本文推导了全贝叶斯RFS滤波器的一种形式，并观察到数据关联隐式存在于类似于MHT的数据结构中。随后，通过近似关联分布得到算法。得到两种算法：一种与JIPDA几乎相同，另一种与MeMBer滤波器相关。两者均在具有挑战性的环境中提升了性能。
英文摘要：
Recent developments in random finite sets (RFSs) have yielded a variety of tracking methods that avoid data association. This paper derives a form of the full Bayes RFS filter and observes that data association is implicitly present, in a data structure similar to MHT. Subsequently, algorithms are obtained by approximating the distribution of associations. Two algorithms result: one nearly identical to JIPDA, and another related to the MeMBer filter. Both improve performance in challenging environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
