论文标题
用于共存点和扩展目标的泊松多晶状体混合物过滤器
A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets
论文作者
论文摘要
本文提出了用于共存点和扩展目标的Poisson Multi-Bernoulli混合物（PMBM）滤波器，即对于可能同时存在和扩展目标的场景。 PMBM过滤器提供了基于数据关联的概率信息以及单目标预测和更新的概率信息来计算多目标过滤后验。在本文中，我们首先得出了广义测量模型的PMBM过滤器更新，该模型可以包括源自点和扩展目标的测量结果。其次，我们提出了一个单一目标空间，该空间可容纳点和扩展目标，并得出过滤递归，该递归递归递归，以传播点目标靶标和伽玛高斯逆终质量密度，以扩展靶标。作为PMBM滤波器的计算有效近似，我们还开发了一个泊松多伯努利（PMB）滤波器，用于共存点和扩展目标。通过数值模拟分析所得的过滤器。
This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i.e., for scenarios where there may be simultaneous point and extended targets. The PMBM filter provides a recursion to compute the multi-target filtering posterior based on probabilistic information on data associations, and single-target predictions and updates. In this paper, we first derive the PMBM filter update for a generalised measurement model, which can include measurements originated from point and extended targets. Second, we propose a single-target space that accommodates both point and extended targets and derive the filtering recursion that propagates Gaussian densities for point targets and gamma Gaussian inverse Wishart densities for extended targets. As a computationally efficient approximation of the PMBM filter, we also develop a Poisson multi-Bernoulli (PMB) filter for coexisting point and extended targets. The resulting filters are analysed via numerical simulations.
