Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum on Commodity Laptops

• URL: http://arxiv.org/abs/2603.10845v1
• Date: Wed, 11 Mar 2026 14:55:15 GMT
• Title: Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum on Commodity Laptops
• Abstract summary: Range-Filtered Doppler Spectrum (RF-DS) is a novel Wi-Fi sensing technique for presence estimation.<n>Our solution can scale across different environments and devices without calibration or retraining.
• Score: 1.3109866252789055
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human Presence Detection (HPD) is key to enable intelligent power management and security features in everyday devices. In this paper we propose the first HPD solution that leverages monostatic Wi-Fi sensing and detects user position using only the built-in Wi-Fi hardware of a device, with no need for external devices, access points, or additional sensors. In contrast, existing HPD solutions for laptops require external dedicated sensors which add cost and complexity, or rely on camera-based approaches that introduce significant privacy concerns. We herewith introduce the Range-Filtered Doppler Spectrum (RF-DS), a novel Wi-Fi sensing technique for presence estimation that enables both range-selective and temporally windowed detection of user presence. By applying targeted range-area filtering in the Channel Impulse Response (CIR) domain before Doppler analysis, our method focuses processing on task-relevant spatial zones, significantly reducing computational complexity. In addition, the use of temporal windows in the spectrum domain provides greater estimator stability compared to conventional 2D Range-Doppler detectors. Furthermore, we propose an adaptive multi-rate processing framework that dynamically adjusts Channel State Information (CSI) sampling rates-operating at low frame rates (10Hz) during idle periods and high rates (100Hz) only when motion is detected. To our knowledge, this is the first low-complexity solution for occupancy detection using monostatic Wi-Fi sensing on a built-in Wi-Fi network interface controller (NIC) of a commercial off-the-shelf laptop that requires no external network infrastructure or specialized sensors. Our solution can scale across different environments and devices without calibration or retraining.

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