Real-time NLOS/LOS Identification for Smartphone-based Indoor Positioning System using WiFi RTT and RSS

TL;DR

This paper presents a real-time machine learning approach using WiFi RTT and RSS to accurately identify NLOS/LOS conditions in indoor positioning, significantly reducing data collection time and latency.

Contribution

It introduces a novel real-time NLOS/LOS identification method with high accuracy and minimal latency using a small sample size and machine learning.

Findings

Achieves about 94% accuracy with only 10 samples

Provides NLOS/LOS identification within 1 second latency

Outperforms existing methods in speed and efficiency

Abstract

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.