AmpN: Real-time LOS/NLOS identification with WiFi

WiFi technology has fostered numerous mobile computing applications, e.g. indoor localization, gesture and activity recognition, device-free localization, etc., due to its ubiquity. The awareness of LOS and NLOS is a prerequisite for WiFi-based methods, since the WiFi signals received under NLOS conditions may contain a lot of noise and multipath effects, exerting great influences on the accuracy of location or identification. Traditional schemes based on commodity WiFi devices can achieve real-time LOS/NLOS identification. However, these methods face the challenges of limited bandwidth and coarse multipath resolution. In this work, we explore the amplitude feature of PHY layer information, and accordingly propose AmpN, a real-time LOS identification scheme based on commodity WiFi infrastructure that is applicable in both static and mobile scenarios. AmpN employs BP neural network algorithm in static scenario and K-Mean method in dynamic scenario, respectively. Experimental results demonstrate that AmpN outperforms existing approaches, achieving overall LOS and NLOS detection rates of 94.2% and 97.6% in static case, and above 97% LOS and NLOS detection rates in mobile context. In addition, the detection delay is less than 0.4s when the link state switches from LOS to NLOS.