Real-Time NLOS/LOS Identification for Smartphone-Based Indoor Positioning Systems Using WiFi RTT and RSS

The accuracy of smartphone-based positioning systems using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research exploits several distribution features a long time series (hundreds samples) received signal strength (RSS) or round-trip (RTT) to achieve high identification accuracy. However, the large sample size attributes power and consumption in data collection for both training testing. This will also undoubtedly be detrimental user experience as waiting getting enough samples is quite long. Therefore, this paper proposes three new real-time NLOS/LOS methods indoor RSS RTT distance measurement (RDM). Based on our extensive analysis RDM dispersion features, machine learning algorithms were chosen developed separate Experiments show that best method achieves discrimination over 96% with 10. Considering theoretically shortest interval 100ms RTT-enabled smartphones, algorithm able provide latency 1s get testing result among all state-of-art methods.