{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T13:04:19Z","timestamp":1763384659365,"version":"3.45.0"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"name":"Hebei University High-level Talents Scientific Research Start-up","award":["521100225229"],"award-info":[{"award-number":["521100225229"]}]},{"DOI":"10.13039\/501100020771","name":"Young Scientists Fund of the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62502214"],"award-info":[{"award-number":["62502214"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Basic Research Program (Natural Science Foundation) of Jiangsu","award":["BK20250727"],"award-info":[{"award-number":["BK20250727"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&D Program of Zhejiang","award":["2023C01029"],"award-info":[{"award-number":["2023C01029"]}]},{"name":"Jiangsu Province Higher Education Basic Science (Natural Science) Research Project","award":["25KJB520026"],"award-info":[{"award-number":["25KJB520026"]}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"crossref","award":["2025r068"],"award-info":[{"award-number":["2025r068"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n                    Accurately estimating the velocity (including speed and direction) of moving targets has recently attracted widespread attention in augmented reality, security monitoring and sports health. In particular, the\u00a0\n                    <jats:italic toggle=\"yes\">Doppler Frequency Shift<\/jats:italic>\n                    (DFS)-based velocity estimation schemes using WiFi devices have shown great potential and have been widely studied. However, previous\u00a0\n                    <jats:italic toggle=\"yes\">Fast Fourier Transform<\/jats:italic>\n                    (FFT)-based and path-parameter-based arts have inherent limitations in DFS estimation and, worse still, ignore the nonlinear measurement errors caused by the relative orientation between the moving target and the WiFi transceiver. The above limitations make it difficult to meet the requirements for fine-grained velocity estimation in practical applications. To cope with these limitations, in this article, we propose a learning-based velocity estimation framework, named\n                    <jats:italic toggle=\"yes\">freeDoppler<\/jats:italic>\n                    , to achieve fine-grained, multi-target and orientation-independent velocity estimation. Specifically, we construct a WiFi-based\u00a0\n                    <jats:italic toggle=\"yes\">Velocity Estimation Network<\/jats:italic>\n                    (VEN), which leverages continuous complex-valued\u00a0\n                    <jats:italic toggle=\"yes\">Channel State Information<\/jats:italic>\n                    (CSI) sequences as input, to fully learn the inherent information of the Doppler effect and accurately predict velocity series. In addition, we adopt the electric field scattering model of Maxwell\u2019s equations to construct a physics-informed\u00a0\n                    <jats:italic toggle=\"yes\">CSI Generation Model<\/jats:italic>\n                    (CGM), thereby generating large-scale and high-quality simulated CSI samples to improve the generalization of the VEN model. Throughout extensive real-world experiments,\n                    <jats:italic toggle=\"yes\">freeDoppler<\/jats:italic>\n                    \u00a0can achieve median errors of 7.98 cm\/s for speed estimation, 28\u00b0 for direction estimation and 35 cm for human tracking in one or two moving targets, significantly outperforming the state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1145\/3772369","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T11:34:35Z","timestamp":1761046475000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["freeDoppler: A Doppler Effect Learning Network for Accurate RF-based Velocity Estimation"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9848-3017","authenticated-orcid":false,"given":"Dawei","family":"Yan","sequence":"first","affiliation":[{"name":"School of Cyber Security and Computer, Hebei University","place":["Baoding, China"]},{"name":"USTC","place":["Baoding, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7555-1232","authenticated-orcid":false,"given":"Feiyu","family":"Han","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0126-6673","authenticated-orcid":false,"given":"Shang","family":"Gao","sequence":"additional","affiliation":[{"name":"USTC","place":["Hefei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5495-8869","authenticated-orcid":false,"given":"Fei","family":"Shang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China","place":["Hefei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1057-2793","authenticated-orcid":false,"given":"Panlong","family":"Yang","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2068-1985","authenticated-orcid":false,"given":"Yubo","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China","place":["Hefei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1137\/141000671"},{"key":"e_1_3_1_3_2","first-page":"1","volume-title":"Proc. of the 11th Mediterranean Conf. on Control and Automation","author":"Bodor Robert","year":"2003","unstructured":"Robert Bodor, Bennett Jackson, and Nikolaos Papanikolopoulos. 2003. 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