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Collaborative signal processing for target tracking in distributed wireless sensor networks

机译:分布式无线传感器网络中用于目标跟踪的协作信号处理

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Target tracking, especially visual target tracking, in complex situations is challenging, which is always performed in single-view system. Because of the conflict between resolution and tracking range, however, single-view tracking is not robust and accurate. This paper presents a distributed multi-view tracking system using collaborative signal processing (CSP) in distributed wireless sensor networks (DWSNs). In the proposed tracking system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas target localization algorithm is carried out by CSP between multisensor. For conquering the disadvantages of client/server based centralized data fusion, a progressive distributed data fusion are proposed. Finally, an indoor target tracking experiment is illustrated, and then tracking performance, execution time and energy consumption of progressive distributed data fusion are compared with client/server based centralized data fusion. Experimental results demonstrate that the CSP based distributed multi-view tracking system in DWSNs can accomplish multi-target extraction, classification, localization, tracking and association quickly and accurately with little congestion, energy consumption and execution time.
机译:目标跟踪,尤其是视觉目标跟踪,在复杂情况下具有挑战性,这总是在单视图系统中执行。但是,由于分辨率和跟踪范围之间存在冲突,因此单视图跟踪并不可靠且准确。本文提出了一种在分布式无线传感器网络(DWSN)中使用协作信号处理(CSP)的分布式多视图跟踪系统。在所提出的跟踪系统中,目标检测和分类算法是基于单节点处理的,目标跟踪是在宿节点中进行的,而目标定位算法是通过CSP在多传感器之间进行的。为了克服基于客户机/服务器的集中式数据融合的缺点,提出了一种渐进式分布式数据融合。最后,说明了室内目标跟踪实验,然后将跟踪性能,执行时间和渐进式分布式数据融合的能耗与基于客户端/服务器的集中式数据融合进行了比较。实验结果表明,DWSNs中基于CSP的分布式多视图跟踪系统可以快速,准确地完成多目标的提取,分类,定位,跟踪和关联,而不会造成拥塞,能耗和执行时间短。

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