ESP32C3 WiFi FTM RSSI Indoor Localization

Wi-Fi FTM RSSI Localization dataset Wi-Fi Fine Time Measurement for positioning / Indoor Localization in 3 different locations and using 8 different APs Custom APs using ESP32C3 and Raw FTM is measured in nanoseconds Data is only measured at the Router Side Data is not measured at client side Has 4 datasets inside the zip folder with over 100,000 data points Contains processed Wi-Fi FTM packets from various routers in: 1. University of Victoria, Engineering Office Wing (EOW) 3rd Floor 2. University of Victoria, Engineering Office Wing (EOW) 5th Floor 3. University of Victoria, Engineering and Computer Science (ECS) 1st Floor Each folder contains a training dataset and a testing dataset that is independent in time and space Router Time is synchronized using chrony Instructions: Dataset is in CSV format Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 ..... Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera Map is in ROS2 PGM format that can read by ROS2 programs Data for the paper Wi-Fi and Bluetooth Contact Tracing Without User Intervention https://ieeexplore.ieee.org/document/9866766 Please Cite As @article{yuen2022wi,
title={Wi-Fi and Bluetooth contact tracing without user intervention},
author={Yuen, Brosnan and Bie, Yifeng and Cairns, Duncan and Harper, Geoffrey and Xu, Jason and Chang, Charles and Dong, Xiaodai and Lu, Tao},
journal={IEEE Access},
volume={10},
pages={91027--91044},
year={2022},
publisher={IEEE}
}