Corpus ID: 220486692

Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers

@article{Lovett2020InferringPF,
  title={Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers},
  author={Tom Lovett and Mark Briers and Marcos Charalambides and Radka Jersakova and James W. Lomax and Chris Holmes},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.05057}
}
  • Tom Lovett, Mark Briers, +3 authors Chris Holmes
  • Published 2020
  • Engineering, Computer Science, Mathematics
  • ArXiv
  • The Covid-19 pandemic has resulted in a variety of approaches for managing infection outbreaks in international populations. One example is mobile phone applications, which attempt to alert infected individuals and their contacts by automatically inferring two key components of infection risk: the proximity to an individual who may be infected, and the duration of proximity. The former component, proximity, relies on Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) as a… CONTINUE READING

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