Identification and mitigation of non-line-of-sight conditions using received signal strength
Identification and mitigation of non-line-of-sight conditions using received signal strength
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DOI：
10.1109/wimob.2013.6673428
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Zhuoling Xiao;Hongkai Wen;A. Markham;A. Trigoni;Phil Blunsom;J. Frolik
中科院分区：
文献类型：
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作者：
Zhuoling Xiao;Hongkai Wen;A. Markham;A. Trigoni;Phil Blunsom;J. Frolik
各种应用（例如人员和物体的定位）可以从非视距（NLOS）识别和缓解技术中受益匪浅。然而，此类技术主要针对超宽带 (UWB) 信号进行研究，而未触及 WiFi 信号区域。在本研究中，我们提出了两种仅使用 WiFi 信号的接收信号强度 (RSS) 测量来识别 NLOS 条件并减轻影响的准确方法。我们首先探索 RSS 中的几个功能，后来证明这些功能在识别和缓解非视距条件方面非常有效。之后，我们根据不同的用户需求和可用信息，基于机器学习技术和假设检验来开发和比较两个主要优化问题。在各种室内环境中进行的大量实验表明，我们的技术不仅可以准确地区分LOS/NLOS条件，而且还可以减轻NLOS条件的影响。
Various applications, such as localisation of persons and objects could benefit greatly from non-line-of-sight (NLOS) identification and mitigation techniques. However, such techniques have been primarily investigated for ultra-wide band (UWB) signals, leaving the area of WiFi signals untouched. In this study, we propose two accurate approaches using only received signal strength (RSS) measurements from WiFi signals to identify NLOS conditions and mitigate the effects. We first explore several features from the RSS which are later demonstrated as very effective in identifying and mitigating NLOS conditions. After that, we develop and compare two major optimization problems based on a machine learning technique and hypothesis testing according to different user requirements and information available. Extensive experiments in various indoor environments have shown that our techniques can not only accurately distinguish between LOS/NLOS conditions, but also mitigate the impact of NLOS conditions as well.