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Dual-Attention and Adversarial Transfer Networks for Sim-to-Real Cross-Orientation Wireless Sensing — arXiv:2608.05664

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05664
License (as archived): CC0-1.0
Archived: 2026-10-31T10:36:04.519Z
SHA-256 of the archived text: 9ef30443db7463086591d7b165a6cdd712088fc0b62749b563e6cf245b95b988
Stable URL of this page: https://metraipolis.com/documento/dc_bb085cd8912d

How to cite

Dual-Attention and Adversarial Transfer Networks for Sim-to-Real Cross-Orientation Wireless Sensing — arXiv:2608.05664 — arXiv (en), https://arxiv.org/abs/2608.05664 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb085cd8912d (Metraipolis archive dc_bb085cd8912d, sha256 9ef30443db7463086591d7b165a6cdd712088fc0b62749b563e6cf245b95b988)

BibTeX

@misc{dcbb085cd8912d,
  title = {Dual-Attention and Adversarial Transfer Networks for Sim-to-Real Cross-Orientation Wireless Sensing — arXiv:2608.05664},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05664},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb085cd8912d; sha256 9ef30443db7463086591d7b165a6cdd712088fc0b62749b563e6cf245b95b988},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05664. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Millimeter-wave human activity recognition suffers significant performance degradation when the user's orientation changes relative to the sensing system, yet collecting labeled multi-orientation data is labor-intensive and costly. To eliminate the need for exhaustive multi-orientation measured data, we develop a physics-guided simulator that synthesizes orientation-diverse wireless training data from single-orientation motion. Specifically, to suppress orientation-induced feature variations, we propose a dual-attention network that extracts activity-discriminative and orientation-robust representations from dual-link Doppler spectrograms. To bridge the simulation-to-reality gap, we introduce an adversarial unsupervised transfer learning mechanism that aligns feature distributions using only a small number of unlabeled target-domain samples. The S2M-Sense platform shows high fidelity in reproducing real-world signatures, validated against 60.48 GHz mmWave measured data with an average structural similarity index measure (SSIM) of 0.84 between simulated and measured Doppler spectrograms across all 4 activities and 4 orientations. Experimental results show that S2M-Sense achieves 88.33% recognition accuracy using only the dual-link multi-orientation simulated dataset, which improves to 95% after simulation-to-reality transfer learning with as few as 16 unlabeled measured samples. Both cases with and without transfer learning outperform state-of-the-art cross-domain sensing methods.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-07 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.05664 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T10:23:30.994Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).