Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
<|reference_start|>Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data: Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition compared with the most recent benchmarks, while still being informative when facing out-of-distribution samples and enabling semantic interpretation of latent variables in terms of physical phenomena. The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i.e., events for which we have limited to no training data.<|reference_end|>
@article{cominelli2024accurate,
    title={Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing
  Data},
    author={Marco Cominelli and Francesco Gringoli and Lance M. Kaplan and Mani B.
  Srivastava and Federico Cerutti},
    journal={arXiv preprint arXiv:2407.04733},
    year={2024},
    doi={10.23919/FUSION52260.2023.10224098},
    archivePrefix={arXiv},
    eprint={2407.04733},
    primaryClass={eess.SP cs.ET cs.LG cs.NI}
}