{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T10:29:06Z","timestamp":1786357746492,"version":"build-2736575974"},"reference-count":31,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62165015"],"award-info":[{"award-number":["62165015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62165015"],"award-info":[{"award-number":["62165015"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned Aerial Vehicle (UAV)-borne laser wavefront sensing technology holds significant application prospects for high-precision vibration detection in the field; however, the acquired signals are highly susceptible to complex, nonlinear environmental noise interference. Existing high-precision deep learning denoising models typically rely on massive computational resources, making them difficult to deploy on resource-constrained edge devices. Consequently, practical engineering exploration is often confined to an inefficient \u201cblind sampling followed by offline processing\u201d mode, incurring a high risk of data invalidation. To explore solutions for real-time quality control at the edge, this paper proposes a lightweight time-frequency state space model (SWIFT-Mamba), aiming to provide an efficient algorithmic foundation and an engineering proof-of-concept for portable devices moving toward in situ verification. Through rigorous evaluation on over 60,000 laboratory-measured and controlled synthetic wavefront vibration data samples, SWIFT-Mamba achieves an average Signal-to-Noise Ratio (SNR) gain of 19.64 dB and a Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) gain of 16.10 dB, with an extremely low computational overhead requiring only 0.066 M parameters and 0.147 GFLOPs. Experimental results demonstrate that while significantly reducing computational costs, the proposed model can effectively extract the physical manifold of the signal and precisely preserve high-frequency phase features.<\/jats:p>","DOI":"10.3390\/s26165048","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:24:53Z","timestamp":1786353893000},"page":"5048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SWIFT-Mamba: A Lightweight State Space Model Toward In Situ Verification of Airborne Wavefront Sensing Signals"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4079-0040","authenticated-orcid":false,"given":"Jianbao","family":"Ma","sequence":"first","affiliation":[{"name":"Key Laboratory of Signal Intelligent Capture and New Generation Communication Technology, School of Electronic Engineering, Yili Normal University, 448 Jiefang Road, Yining 835000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Intelligent Capture and New Generation Communication Technology, School of Electronic Engineering, Yili Normal University, 448 Jiefang Road, Yining 835000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiyou","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Intelligent Capture and New Generation Communication Technology, School of Electronic Engineering, Yili Normal University, 448 Jiefang Road, Yining 835000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Optical Sensing and Manipulation, College of Engineering & Applied Sciences, Ministry of Education, Nanjing University, 163 Xianlin Ave, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinshan","family":"Su","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Intelligent Capture and New Generation Communication Technology, School of Electronic Engineering, Yili Normal University, 448 Jiefang Road, Yining 835000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Luo, Q., Luo, H., Wu, G., Ji, X., Su, J., and Jiang, W. 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