Lightweight Range-Angle Imaging Based Algorithm for Quasi-Static Human Detection on Low-Cost FMCW Radar

TL;DR

This paper introduces a lightweight, non-visual radar-based method for detecting static human presence in indoor environments, achieving high accuracy and real-time performance on low-cost hardware.

Contribution

It presents a novel, simple image-based algorithm for quasi-static human detection using FMCW radar, significantly improving accuracy and speed over traditional CFAR detectors.

Findings

Detection accuracy improved to over 93% for all subjects.

Achieved over 120 frames per second on Raspberry Pi 4B.

Real-time processing with 8.2 ms per frame.

Abstract

Quasi-static human activities such as lying, standing or sitting produce very low Doppler shifts and highly spread radar signatures, making them difficult to detect with conventional constant-false-alarm rate (CFAR) detectors tuned for point targets. Moreover, privacy concerns and low lighting conditions limit the use of cameras in long-term care (LTC) facilities. This paper proposes a lightweight, non-visual image-based method for robust quasi-static human presence detection using a low-cost 60 GHz FMCW radar. On a dataset covering five semi-static activities, the proposed method improves average detection accuracy from 68.3% for Cell-Averaging CFAR (CA-CFAR) and 78.8% for Order-Statistics CFAR (OS-CFAR) to 93.24% for Subject 1, from 51.3%, 68.3% to 92.3% for Subject 2, and 57.72%, 69.94% to 94.82% for Subject 3, respectively. Finally, we benchmarked all three detectors across all activities on a Raspberry Pi 4B using a shared Range-Angle (RA) preprocessing pipeline. The proposed algorithm obtains an average 8.2 ms per frame, resulting in over 120 frames per second (FPS) and a 74 times speed-up over OS-CFAR. These results demonstrate that simple image-based processing can provide robust and deployable quasi-static human sensing in cluttered indoor environments.