用于降维估计融合的航迹-航迹关联
Track-To-Track Association for Fusion of Dimension-Reduced Estimates
AI总结：
针对通信约束下交换降维航迹估计的网络中心多目标跟踪，提出依据关联性能进行降维的新形式化与优化策略，并通过理论分析和多目标数值实验验证其可保留关联质量。
摘要：
本文考虑通信约束下的以网络为中心的多目标跟踪，其中各节点交换降维后的航迹估计。该子领域中以往关于目标跟踪的工作仅关注融合方面，并基于融合性能推导了降低维度的最优方式。在本工作中，我们提出一种新颖的问题形式化，其中根据关联性能来降低估计的维度。对该问题进行了理论分析，并推导了问题性质。理论分析引出一种优化策略，可在降低所通信估计的维度时用于部分保留关联质量。所建议优化策略的适用性在一个多目标场景中通过数值方式得到证明。
英文摘要：
Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.
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。
