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Multiple-target tracking based on compressed sensing in the Internet of Things

机译:物联网中基于压缩感知的多目标跟踪

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摘要

An algorithm based on compressed sensing for tracking multiple targets in the Internet of Things (IoT) is proposed in this study. First, we build a sparse representation of the changes in the sampling signal caused by nodes to multiple targets in a monitored area. Second, we observe the sampling of the sensing signals by nodes to mobile targets and reconstruct the sampling subtraction data. Third, we use sampling subtraction, which is extended by a background subtraction technique in video target tracking, to obtain useful tracking data with sampling subtraction data and to locate the mobile targets. Simulation results show that the proposed algorithm recovers the sensing signal with sparse sampling subtraction data, accurately locates multiple targets, significantly reduces network communication traffic, and improves the energy efficiency of the system with the sparse sampling strategy.
机译:提出了一种基于压缩感知的跟踪物联网中多个目标的算法。首先,我们建立了一个稀疏表示,表示由节点到受监控区域中多个目标的节点引起的采样信号的变化。其次,我们观察节点对移动目标的传感信号采样,并重建采样减法数据。第三,我们将采样减法(通过背景减法技术扩展到视频目标跟踪中)用于获取具有采样减法数据的有用跟踪数据并定位移动目标。仿真结果表明,该算法利用稀疏采样减法数据恢复传感信号,准确定位多个目标,显着减少网络通信流量,并采用稀疏采样策略提高了系统的能效。

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