面向轨迹集合的马尔可夫链蒙特卡洛多扫描数据关联
Markov Chain Monte Carlo Multi-Scan Data Association for Sets of Trajectories
AI总结：
针对多目标跟踪的批量求解需求，提出两种基于MCMC采样的离线TPMBM滤波器实现，可在全时间区间求解多扫描数据关联问题，充分利用测量信息，其中采用Metropolis-Hastings算法的方案达到了当前最优的多轨迹估计性能。
摘要：
本文研究基于轨迹集合的多目标跟踪问题的批量求解方案。具体而言，我们提出了两种面向批量数据的轨迹泊松多伯努利混合（TPMBM）滤波器离线实现方案，其核心是对数据关联假设进行马尔可夫链蒙特卡洛（MCMC）采样。与在线TPMBM实现不同，所提出的离线实现方案在整个感兴趣时间区间内求解大规模多扫描数据关联问题，因此能够充分利用所有可用的测量信息。此外，通过借助TPMBM滤波器高效的假设结构，所提出的实现方案性能优于其他基于MCMC的多目标跟踪算法。仿真结果表明，采用Metropolis-Hastings算法的TPMBM实现具备当前最优的多轨迹估计性能。
英文摘要：
This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories. Specifically, we present two offline implementations of the trajectory Poisson multi-Bernoulli mixture (TPMBM) filter for batch data based on Markov chain Monte Carlo (MCMC) sampling of the data association hypotheses. In contrast to online TPMBM implementations, the proposed offline implementations solve a large-scale, multi-scan data association problem across the entire time interval of interest, and therefore they can fully exploit all the measurement information available. Furthermore, by leveraging the efficient hypothesis structure of TPMBM filters, the proposed implementations compare favorably with other MCMC-based multi-object tracking algorithms. Simulation results show that the TPMBM implementation using the Metropolis-Hastings algorithm presents state-of-the-art multiple trajectory estimation performance.
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。
