融合粒子与高斯混合的多目标滤波器，提升跟踪精度。
Kernel-Based Ensemble Gaussian Mixture Probability Hypothesis Density Filter
- 用核密度估计从后验分布采样粒子，再构建先验高斯混合
- 相同粒子数下，性能优于传统GM-PHD和SMC-PHD滤波器
- 适用于复杂多目标场景，尤其适合需要高精度的跟踪任务
本文提出一种基于核函数的集成高斯混合概率假设密度（EnGM-PHD）滤波器，用于多目标跟踪。该方法结合了基于高斯混合的GM-PHD滤波器与基于粒子的SMC-PHD滤波器的优点：从后验强度函数中获取粒子，通过系统动态传播，并利用核密度估计（KDE）近似先验强度函数的高斯混合形式。该方法在分量数量趋于无穷时可收敛至真实强度函数。当仅存在单一目标且无出生、死亡、杂波及完美检测时，该滤波器退化为标准的集成高斯混合滤波器（EnGMF）。实验表明，在使用相同数量的分量或粒子条件下，该方法在多目标跟踪任务中的性能优于GM-PHD和SMC-PHD滤波器。
原文摘要 · Abstract (English)
In this work, a kernel-based Ensemble Gaussian Mixture Probability Hypothesis Density (EnGM-PHD) filter is presented for multi-target filtering applications. The EnGM-PHD filter combines the Gaussian-mixture-based techniques of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with the particle-based techniques of the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. It achieves this by obtaining particles from the posterior intensity function, propagating them through the system dynamics, and then using Kernel Density Estimation (KDE) techniques to approximate the Gaussian mixture of the prior intensity function. This approach guarantees convergence to the true intensity function in the limit of the number of components. Moreover, in the special case of a single target with no births, deaths, clutter, and perfect detection probability, the EnGM-PHD filter reduces to the standard Ensemble Gaussian Mixture Filter (EnGMF). In the presented experiment, the results indicate that the EnGM-PHD filter achieves better multi-target filtering performance than both the GM-PHD and SMC-PHD filters while using the same number of components or particles.
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