集群传感器网络中的多目标跟踪基于标记的多伯诺利选
Yuqin Zhou1, Liping Yan1, Hui Li2
1Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China.
ISA transactions
|August 7, 2025
概括
这项研究引入了集群传感器网络的新型多目标跟踪 (MTT) 算法,尽管传感器视图不一致和标签错误,但提高了准确性. 该方法在复杂的网络环境中提高了跟踪性能.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 信号处理 信号处理
背景情况:
- 传感器网络中的多目标跟踪 (MTT) 面临来自不一致的传感器视野和目标标签错误的挑战.
- 这些问题在集群传感器网络架构中加剧,降低了整体跟踪精度.
- 现有的方法往往很难有效地处理这些组合的不准确性.
研究的目的:
- 为集群传感器网络开发一个强大的MTT算法,以解决传感器视野和目标标签中的不一致性.
- 在复杂,异质的传感器环境中提高多目标跟踪的准确性和可靠性.
- 为整合多传感器数据和减轻标签错误提供系统方法.
主要方法:
- 使用标记的测量和预测信息,在每个集群头 (CH) 设置一个多传感器测量假设.
- 通过将假设集与融合方法集成,生成一个多传感器聚变测量集.
- 在CH中使用标记的多伯诺利波器实施本地MTT过程.
- 通过非反融合机制完成全球MTT过程,将单个CHs的结果结合起来.
主要成果:
- 拟议的算法有效地处理来自有不一致视野的传感器的测量信息.
- 整合标记的多伯诺利波器和无反的聚变机制可以减轻目标匹配和标记错误.
- 实验模拟验证了开发的MTT算法的卓越性能和有效性.
结论:
- 介绍的MTT算法显著提高了在集群传感器网络中的跟踪准确性,其中有传感器视图和标签不一致.
- 这种新的方法为分布式传感器系统中复杂的多目标跟踪场景提供了强大的解决方案.
- 这些发现表明,在依赖精确传感器网络数据的应用中,增强情境意识的潜力.
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