论文标题
带有一般目标生成的测量和任意混乱的泊松多晶状体混合物过滤器
Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter
论文作者
论文摘要
本文表明，泊松多伯努利混合物（PMBM）密度是一般目标生成的测量分布和任意杂波分布的多目标共轭物。也就是说，对于这种多目标测量模型和带有泊松出生模型的标准多目标动态模型，预测和过滤密度是PMBM。我们得出相应的PMBM过滤递归。基于此结果，我们实施了一个PMBM滤波器，以进行点目标测量模型和负二项式杂波密度，其中数据关联假设通过Gibbs采样选择具有较高权重的情况。我们还用杂物实现了扩展的目标PMBM滤波器，即Poisson分布的混乱和有限数量的独立混乱来源的结合。仿真结果表明，拟议过滤器的好处是处理非标准的混乱。
This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions. That is, for this multi-target measurement model and the standard multi-target dynamic model with Poisson birth model, the predicted and filtering densities are PMBMs. We derive the corresponding PMBM filtering recursion. Based on this result, we implement a PMBM filter for point-target measurement models and negative binomial clutter density in which data association hypotheses with high weights are chosen via Gibbs sampling. We also implement an extended target PMBM filter with clutter that is the union of Poisson-distributed clutter and a finite number of independent clutter sources. Simulation results show the benefits of the proposed filters to deal with non-standard clutter.
