  The accuracy of smartphone-based positioning methods using WiFi usually
suffers from ranging errors caused by non-line-of-sight (NLOS) conditions.
Previous research usually exploits several statistical features from a long
time series (hundreds of samples) of WiFi received signal strength (RSS) or
WiFi round-trip time (RTT) to achieve a high identification accuracy. However,
the long time series or large sample size attributes to high power and time
consumption in data collection for both training and testing. This will also
undoubtedly be detrimental to user experience as the waiting time of getting
enough samples is quite long. Therefore, this paper proposes a new real-time
NLOS/LOS identification method for smartphone-based indoor positioning system
using WiFi RTT and RSS. Based on our extensive analysis of RSS and RTT
features, a machine learning-based method using random forest was chosen and
developed to separate the samples for NLOS/LOS conditions. Experiments in
different environments show that our method achieves a discrimination accuracy
of about 94% with a sample size of 10. Considering the theoretically shortest
WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is
able to provide the shortest latency of 1s to get the testing result among all
of the state-of-art methods.
