论文标题
数据驱动的聚类和Bernoulli合并用于Poisson Multi-Bernoulli混合物过滤器
Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter
论文作者
论文摘要
本文提出了用于泊松多伯努利混合物（PMBM）过滤器的聚类和合并方法，以降低其计算复杂性，并使其适合具有大量目标的多个目标跟踪。我们定义了一种测量驱动的聚类算法，以将数据关联问题减少到几个子问题中，并通过kullback-leibler Divergence最小化提供了所得聚类的PMBM后密度的推导。此外，我们通过通过合并和轨道交换伯努利组件近似后近近似后，研究了不同的策略，以减少单个目标假设的数量。我们评估了具有超过一千个目标的模拟跟踪方案上提出的算法的性能。
This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.
