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Particle-gating SMC-PHD filter

机译：颗粒门SMC-PHD过滤器

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摘要

The Sequential Monte Carlo (SMC) implementation for the probability hypothesis density (PHD) filter, referred to as the SMC-PHD filter, is a good candidate for multi-target tracking (MTT) problems. It recursively propagates the weighted particle set that approximates the multi-target posterior density. In this paper, we propose an improved SMC-PHD filter for MTT called the particle-gating SMC-PHD filter. First, a robust gating based on particles propagated from a previous time period is proposed to select the observations of survival targets. Second, a sigma-nearest-gating is proposed to accurately select the observations of new targets. By employing only the observations obtained by the above algorithms to update the state estimations, the overall processing speed of the filter is significantly improved. In addition, a softening factor is suggested to lower the average number of clutters in the updater. This provides more accurate estimation compared with the basic SMC-PHD filter. Finally, the respective realtime and tracking performances of the proposed SMC-PHD filter are verified by the simulation results.
机译：概率假设密度（PHD）过滤器的顺序蒙特卡洛（SMC）实现（称为SMC-PHD过滤器）是解决多目标跟踪（MTT）问题的理想选择。它递归地传播近似多目标后验密度的加权粒子集。在本文中，我们为MTT提出了一种改进的SMC-PHD滤波器，称为粒子门SMC-PHD滤波器。首先，提出了基于从先前时间段传播的粒子的鲁棒门控，以选择对生存目标的观察。其次，提出了sigma-neast-gate来准确选择新目标的观测值。通过仅采用上述算法获得的观察值来更新状态估计，可以显着提高滤波器的整体处理速度。此外，建议使用软化因子来降低更新程序中平均混乱的数量。与基本的SMC-PHD滤波器相比，这提供了更准确的估计。最后，仿真结果验证了所提出的SMC-PHD滤波器的实时性和跟踪性能。

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