Lei Qian, Zhang Haijian, Sun Hong, Tang Linling
School of Electronic Information, Wuhan University, 430072 Wuhan, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079 Wuhan, China.
Sensors (Basel). 2016 Apr 22;16(4):577. doi: 10.3390/s16040577.
Device-free localization (DFL) based on wireless sensor networks (WSNs) is expected to detect and locate a person without the need for any wireless devices. Radio tomographic imaging (RTI) has attracted wide attention from researchers as an emerging important technology in WSNs. However, there is much room for improvement in localization estimation accuracy. In this paper, we propose a geometry-based elliptical model and adopt the orthogonal matching pursuit (OMP) algorithm. The new elliptical model uses not only line-of-sight information, but also non-line-of-sight information, which divides one ellipse into several areas with different weights. Meanwhile the OMP, which can eliminate extra bright spots in image reconstruction, is used to derive an image estimator. The experimental results demonstrate that the proposed algorithm could improve the accuracy of positioning by up to 23.8% for one person and 33.3% for two persons over some state-of-the-art RTI methods.
基于无线传感器网络(WSN)的无设备定位(DFL)有望在无需任何无线设备的情况下检测并定位人员。作为WSN中一项新兴的重要技术,无线电层析成像(RTI)已引起研究人员的广泛关注。然而,在定位估计精度方面仍有很大的提升空间。在本文中,我们提出了一种基于几何的椭圆模型,并采用正交匹配追踪(OMP)算法。新的椭圆模型不仅使用视距信息,还使用非视距信息,它将一个椭圆划分为几个具有不同权重的区域。同时,可消除图像重建中额外亮点的OMP被用于推导图像估计器。实验结果表明,与一些最先进的RTI方法相比,所提算法可将单人定位精度提高多达23.8%,双人定位精度提高33.3%。