Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data

• URL: http://arxiv.org/abs/2407.04733v1
• Date: Mon, 1 Jul 2024 08:26:15 GMT
• Title: Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
• Authors: Marco Cominelli, Francesco Gringoli, Lance M. Kaplan, Mani B. Srivastava, Federico Cerutti,
• Abstract summary: 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 a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition.
• Score: 12.511211994847173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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