تقرير
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
| العنوان: | Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data |
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| المؤلفون: | Cominelli, Marco, Gringoli, Francesco, Kaplan, Lance M., Srivastava, Mani B., Cerutti, Federico |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing, Computer Science - Emerging Technologies, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture |
| الوصف: | 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. Comment: 8 pages, 10 figures, accepted at 26th International Conference on Information Fusion (FUSION 2023) |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.23919/FUSION52260.2023.10224098 |
| URL الوصول: | http://arxiv.org/abs/2407.04733 |
| رقم الأكسشن: | edsarx.2407.04733 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.23919/FUSION52260.2023.10224098 |
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