算术平均密度融合——第三部分：异构无标记与有标记RFS滤波器融合
Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion
AI总结：
本研究针对多传感器多目标跟踪中异构RFS滤波器的密度融合问题，提出基于高斯混合的PHD共识坐标下降法，仅修正局部高斯分量权重，有效实现PHD-MB-LMB滤波器的同构与异构融合。
摘要：
本文针对可扩展多传感器多目标跟踪场景，提出了一种异构密度融合方法。在该场景中，互连的传感器可根据自身算力与需求运行不同类型的随机有限集（RFS）滤波器。这些不同的RFS滤波器会生成异构多目标密度，需通过合理方式相互融合，以实现更鲁棒、准确的目标检测与定位。本方法基于高斯混合实现，对局部高斯分量（L-GCs）进行修正以达成PHD共识，即无论局部密度的具体类型如何，各滤波器对应的无标记概率假设密度（PHD）都能最佳拟合其平均值。为此，本文提出了一种计算高效的坐标下降法，仅修正L-GCs的权重，保持其他参数不变。研究具体涉及PHD滤波器、无标记与有标记多伯努利（MB/LMB）滤波器。仿真实验验证了所提方法在不同配置下的PHD-MB-LMB滤波器同构与异构融合中的有效性。
英文摘要：
This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets. Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities. To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged. In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered. Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
