@inproceedings{1008,
  author = {Zongxing Xie and Fan Ye},
  title = {Poster Abstract: Scaling Device-free Indoor Tracking based on Self Calibration},
  abstract = {The democratization of indoor tracking systems lays the ground- work for a wide spectrum of smart home applications. While prior work on RF-based device-free localization/tracking have shown preferable features and promising results, they heavily relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, making it prohibitively expensive to scale deployments to wide range (e.g., tens or hundreds of real homes). We propose SCALING, a plug-and-play indoor tracking system, of which the key enabler is a self calibrating algorithm that estimates the distributed sensor locations through their distance measurements to a person walking a trajectory, a trivial effort without taxing layman users physically or cognitively. We have experimentally evaluated SCALING via real world testbeds and shown an 80-percentile tracking accuracy of 40.5 cm, only 1% degradation compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort.},
  year = {2022},
  journal = {ACM Conference on Embedded Networked Sensor Systems},
  month = {01},
  url = {https://par.nsf.gov/biblio/10439724},
}