发表机构
University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有标记随机有限集滤波器无法表示协作群体运动的问题，提出一种将联合粒子滤波与集成高斯混合滤波耦合的新滤波器，可更新每个粒子处的密度，并在粒子数极限下收敛于真实贝叶斯后验，实验验证了其有效性。
AI 中文摘要
针对每个目标的标记随机有限集滤波器，例如最先进的$\delta$-广义标记多伯努利滤波器，无法表示目标之间协作的、群体式的运动。在这项工作中，我们提出了一种能够表示这种运动的新滤波器。新的粒子滤波器将联合粒子滤波器与集成高斯混合滤波器耦合，使得一次测量更新每个粒子处的密度，而不仅仅是重新加权。我们证明了在粒子数极限下，新的粒子滤波器收敛于联合粒子滤波器，从而在特定假设下收敛于真实的贝叶斯后验。在两个耦合系统（Vicsek模型和耦合布朗运动）上的实验结果为该滤波器的有效性提供了证据。
英文摘要
Per-target labeled random-finite-set filters, such as the state of the art $δ$-generalized labeled multi-Bernoulli filter, cannot represent cooperating, swarm-like, motion between targets. In this work, we present a new filter that can. The new particle filter couples the joint particle filter with ensemble Gaussian mixture filter such that a measurement updates the density at every particle instead of merely reweighting. We prove that the new particle filter converges to the joint particle filter in the limit of particle number and thus to the true Bayesian posterior, under certain assumptions. Experimental results on two coupled systems, the Vicsek model and coupled Brownian motion provide evidence on the efficacy of the filter.
