PRIVAtE: Passive Radar Interpretability using Variational Auto Encoders
Marco Cominelli, Paolo Braca, Leonardo Maria Millefiori, Lance Kaplan, Mani B. Srivastava, Francesco Gringoli, Federico Cerutti · 2023
This paper aims to present a method to increase interpretability human analysts can have in actionable intelligence from analysing Wi-Fi signals used as passive radar systems for situational understanding. The Passive Radar Interpretability using Variational Auto Encoders (PRIVAtE) method is demonstrated using a recent dataset that estimates the latent distributions of antennas of the same Wi-Fi receiver to perform human activity recognition. The performance theoretical analysis of machine learning binary classification includes error probabilities using a statistical test based on a classification learned in the training phase. Results demonstrate that the compressed data obtained using the Variational Auto-Encoder is statistically very informative for providing situational understanding.