WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

Abstract

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research.