{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:07:36Z","timestamp":1788865656044,"version":"build-2803163510"},"reference-count":24,"publisher":"AIP Publishing","issue":"1","funder":[{"name":"Fundamental Research Program of Shanxi Province","award":["202203021221114"],"award-info":[{"award-number":["202203021221114"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["202203021212141"],"award-info":[{"award-number":["202203021212141"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["202303021211163"],"award-info":[{"award-number":["202303021211163"]}]}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,1,1]]},"abstract":"<jats:p>Real-time moving target trajectory prediction is highly valuable in applications such as automatic driving, target tracking, and motion prediction. This paper examines the projection of three-dimensional random motion of an object in space onto a sensing plane as an illustrative example. Historical running trajectory data are used to train a reserve network. The trained network model is subsequently used to predict future trajectories. In the experiment, a network model trained on 20\u2009000 frames of random running trajectory data was used to predict trajectories for 1\u201320 future frames, and 5000 frames were used for testing. The results showed prediction errors for 80% of the predictions of less than 0.01%, 0.8%, and 4% for 1, 10, and 20 future frames, respectively.<\/jats:p>","DOI":"10.1063\/5.0233064","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T13:41:51Z","timestamp":1737466911000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":3,"title":["Real-time motion trajectory training and prediction using reservoir computing for intelligent sensing equipment"],"prefix":"10.1063","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8094-7897","authenticated-orcid":false,"given":"Yuru","family":"Mao","sequence":"first","affiliation":[{"name":"Shanxi Key Laboratory of Intelligent Detection Technology and Equipment, School of Information and Communication Engineering, North University of China , Taiyuan 030051, Shanxi,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8849-9077","authenticated-orcid":false,"given":"Ning","family":"Jing","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Intelligent Detection Technology and Equipment, School of Information and Communication Engineering, North University of China , Taiyuan 030051, Shanxi,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjie","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Intelligent Detection Technology and Equipment, School of Information and Communication Engineering, North University of China , Taiyuan 030051, Shanxi,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"issue":"3","key":"2025012113414906900_c1","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/tiv.2022.3167103","article-title":"A survey on trajectory-prediction methods for autonomous driving","volume":"7","year":"2022","journal-title":"IEEE Trans. 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