2 research outputs found
Sensor-Aided Learning for Wi-Fi Positioning with Beacon Channel State Information
Because each indoor site has its own radio propagation characteristics, a
site survey process is essential to optimize a Wi-Fi ranging strategy for
range-based positioning solutions. This paper studies an unsupervised learning
technique that autonomously investigates the characteristics of the surrounding
environment using sensor data accumulated while users use a positioning
application. Using the collected sensor data, the device trajectory can be
regenerated, and a Wi-Fi ranging module is trained to make the shape of the
estimated trajectory using Wi-Fi similar to that obtained from sensors. In this
process, the ranging module learns the way to identify the channel conditions
from each Wi-Fi access point (AP) and produce ranging results accordingly.
Furthermore, we collect the channel state information (CSI) from beacon frames
and evaluate the benefit of using CSI in addition to received signal strength
(RSS) measurements. When CSI is available, the ranging module can identify more
diverse channel conditions from each AP, and thus more precise positioning
results can be achieved. The effectiveness of the proposed learning technique
is verified using a real-time positioning application implemented on a PC
platform.Comment: 13 pages, 16 figures, submitted to an IEEE journal for publication,
demo video is available: https://youtu.be/-6vfLEBS9M
Calibration-Free Positioning Technique Using Wi-Fi Ranging and Built-in Sensors of Mobile Devices
As positioning solutions integrate multiple components to improve accuracy,
the number of parameters that require calibration has increased. This paper
studies a calibration-free positioning technique using Wi-Fi ranging and
pedestrian dead reckoning (PDR), where every parameter in the system is
optimized in real-time. This significantly decreases the time and effort
required to perform manual calibration procedures and enables the positioning
solution to achieve robust performance in various situations. Additionally,
this paper studies an efficient way of performing irregular Wi-Fi ranging
procedures to improve battery life and network performance of mobile devices.
The positioning performance of the proposed method was verified using a
real-time Android application on several mobile devices under a large indoor
office environment. Without any calibration, the proposed method achieved up to
1.38 m average positioning accuracy for received signal strength (RSS)-based
ranging scenarios, which differs only by 30 cm from the benchmark assuming
perfect calibration. In addition, the proposed method achieved up to 1.04 m
accuracy for round trip time (RTT)-based ranging scenarios with a 40 MHz
bandwidth configuration, which differs only by 10 cm from the benchmark.Comment: 14 pages, 16 figures, demo video is available:
https://youtu.be/_FnznD1gxV