Monostatic DMG Passive Sensing with Hypothesis Testing

This paper considers object detection with millimeter-wave (mmWave) Wi-Fi beam training frames, e.g., beacon frames, in a monostatic passive directional multi-gigabit (DMG) sensing configuration. We derive an explicit signal model that accounts for the preamble, frame-to-frame antenna gains, and clutter. Given the signal model, we develop a hypothesis testing-based object detection that directly leverages symbol-level preamble waveforms and explores the Kronecker structure between the range steering vector and the Doppler steering vector weighted by the antenna gain. Numerical results confirm the effectiveness of the proposed detector and evaluate the impact of frame-to-frame antenna gains due to the beam scanning.