arXiv 2309.017742023-09eess.SP

密集杂波中近距离目标的变分跟踪与重检测：补充材料

Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter: Supplementary Materials

Runze Gan, Qing Li, Simon Godsill

备注：: Supplementary Materials, including Appendices C-F, begin on page 25, with pages 1-24 constituting the main article. Key updates from the first arXiv version include: added comparisons of the sum-product-algorithm-based tracker, included Appendix C for association update derivation, and made minor adjustments throughout the article to enhance presentation

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对密集近距离目标和严重杂波场景，本文基于变分滤波框架提出VB-AbNHPP跟踪器，结合变分定位策略实现目标重检测与航迹恢复，在精度和效率上优于现有方法。

摘要：

非齐次泊松过程（NHPP）是一种广泛使用的测量模型，允许目标随时间产生多个测量值。然而，在密集近距离目标和严重杂波的场景下，基于该NHPP模型高效可靠地跟踪多个目标十分困难。因此，基于通用坐标上升变分滤波框架，本文提出了一种基于变分贝叶斯关联的NHPP跟踪器（VB-AbNHPP），能够通过可并行化的实现，高效执行跟踪、数据关联以及目标与杂波率的学习。此外，提出了一种变分定位策略，能够在极严重杂波下从大范围监视区域中快速重新发现遗漏的目标。该策略被集成到VB-AbNHPP跟踪器中，形成了一种能够自动检测并从航迹丢失中恢复的稳健方法。在具有挑战性的场景中，与现有跟踪器相比，该跟踪器在准确性和效率方面均展现出更好的跟踪性能。

英文摘要：

The non-homogeneous Poisson process (NHPP) is a widely used measurement model that allows for an object to generate multiple measurements over time. However, it can be difficult to efficiently and reliably track multiple objects under this NHPP model in scenarios with a high density of closely-spaced objects and heavy clutter. Therefore, based on the general coordinate ascent variational filtering framework, this paper presents a variational Bayes association-based NHPP tracker (VB-AbNHPP) that can efficiently perform tracking, data association, and learning of target and clutter rates with a parallelisable implementation. In addition, a variational localisation strategy is proposed, which enables rapid rediscovery of missed targets from a large surveillance area under extremely heavy clutter. This strategy is integrated into the VB-AbNHPP tracker, resulting in a robust methodology that can automatically detect and recover from track loss. This tracker demonstrates improved tracking performance compared with existing trackers in challenging scenarios, in terms of both accuracy and efficiency.

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。