Real-time NLOS/LOS Identification for Smartphone-based Indoor Positioning System using WiFi RTT and RSS
Yinhuan Dong, Tugrul Arslan

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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Speech and Audio Processing · Advanced Adaptive Filtering Techniques
