Identification and mitigation of non-line-of-sight conditions using received signal strength

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.