School of Internet of Things Engineering, Jiangnan University; Wuxi 214122, China.
Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Korea.
Sensors (Basel). 2018 Sep 29;18(10):3276. doi: 10.3390/s18103276.
Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance.
无线传感器网络（WSNs）环境中的目标跟踪技术分为主动和被动两种方案。与需要目标持有合作设备的主动定位方案不同，最近对被动跟踪（即跟踪无设备目标）的研究显示出了很大的前景。在 WSN 中，可以通过感知视线链路（LOSL）上的射频层析成像（RFT）来跟踪无设备目标。在本文中，我们提出了一种利用自适应网络 LOSL 网络和几何约束方法的被动跟踪方案，用于跟踪单个目标和多个目标。关于基础知识，我们首先探索了用于实际情况中 RFT 检测的空间分集技术。然后，我们分析了 WSN 的能量消耗，并提出了一种自适应网络方案，以达到节能的目的。与保持固定 LOSL 密度的方案不同，所提出的方案可以自适应地调整网络级别，在保证跟踪精度的同时节省能量。通过计算机仿真评估了所提出方案的有效性。结果表明，所提出的方案可以充分降低功耗，同时提供合格的跟踪性能。
