This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and an arbitrary clutter distribution. 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.
翻译:本文显示, Poisson 多- Bernoulli 混合物(PMBM) 密度是通用目标生成测量分布和任意布局分布之前的多目标组合。 也就是说, 对于这个多目标测量模型和配有Poisson 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过滤循环。 基于此结果, 我们为点目标测量模型和负二元结裂密度实施了 PMBM 过滤器, 通过 Gibbs 取样选择高重量数据关联的假设。 我们还实施了一个扩展目标PMBM过滤器, 配有布局, 即Poisson 分布布局的组合和数量有限的独立布局源。 模拟结果显示了拟议过滤器的好处 。