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Multitarget State Extraction for the PHD Filter using MCMC Approach

机译：使用MCMC方法提取PHD滤波器的多目标状态

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摘要

It is known that multitarget states cannot be directly derived from the particle probability hypothesis density (particle-PHD) filter. Therefore, some cluster algorithms are used to extract the states from the particles. Actually, these algorithms become a crucial step in how to cluster the particles effectively and robustly in the particle-PHD filter. A novel multitarget state extraction algorithm for the particle-PHD filter is proposed. The proposed algorithm is comprised of two steps. First, the target number is calculated via the particle-PHD filter. Second, the distribution of the particles is fitted using finite mixture models (FMMs), whose parameters can be derived using a Markov chain Monte Carlo (MCMC) sampling scheme. Then the states can be extracted according to the fitted mixture distribution. The final simulations show that the proposed algorithm is effective for the extraction of the individual states even when the clutter is dense and the distribution of the particles is relatively complex.
机译：众所周知，多目标状态不能直接从粒子概率假设密度（粒子-PHD）滤波器中导出。因此，一些聚类算法用于从粒子中提取状态。实际上，这些算法已成为如何在粒子PHD滤波器中有效而稳健地对粒子进行聚类的关键步骤。提出了一种新的粒子PHD滤波器多目标状态提取算法。所提出的算法包括两个步骤。首先，通过粒子-PHD滤波器计算目标数。其次，使用有限混合模型（FMM）拟合粒子的分布，可以使用马尔可夫链蒙特卡洛（MCMC）采样方案导出其参数。然后可以根据拟合的混合物分布提取状态。最终的仿真结果表明，即使杂波密集且粒子的分布相对复杂，所提出的算法也能有效地提取各个状态。

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