Lightweight Range-Angle Imaging Based Algorithm for Quasi-Static Human Detection on Low-Cost FMCW Radar
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910023064485888 |
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| author | Trinh, Huy Shaker, George |
| author_facet | Trinh, Huy Shaker, George |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14001 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lightweight Range-Angle Imaging Based Algorithm for Quasi-Static Human Detection on Low-Cost FMCW Radar Trinh, Huy Shaker, George Signal Processing 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. |
| title | Lightweight Range-Angle Imaging Based Algorithm for Quasi-Static Human Detection on Low-Cost FMCW Radar |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.14001 |