Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914859865604096 |
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| author | Cominelli, Marco Gringoli, Francesco Kaplan, Lance M. Srivastava, Mani B. Cerutti, Federico |
| author_facet | Cominelli, Marco Gringoli, Francesco Kaplan, Lance M. Srivastava, Mani B. Cerutti, Federico |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04733 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data Cominelli, Marco Gringoli, Francesco Kaplan, Lance M. Srivastava, Mani B. Cerutti, Federico Signal Processing Emerging Technologies Machine Learning Networking and Internet Architecture 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. |
| title | Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data |
| topic | Signal Processing Emerging Technologies Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2407.04733 |