@inproceedings{1007,
  author = {Zongxing Xie and Fan Ye},
  title = {Self-Calibrating Indoor Trajectory Tracking System Using Distributed Monostatic Radars For Large Scale Deployment},
  abstract = {24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly re- searchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We present SCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily setup by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluate SCALING via simulations and two testbeds (in lab and home configurations of sizes 3 × 6 sq m and 4.5 × 8.5 sq m). Experimental results demonstrate that SCALING outperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking,respectively.Notably,only1% degradation in performance has been observed with SCALING compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort.},
  year = {2022},
  journal = {ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
  month = {01},
  url = {https://par.nsf.gov/biblio/10439770},
  doi = {10.1145/3563357.3564078},
}