{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T09:26:41Z","timestamp":1762075601509,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation Council of China","award":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"],"award-info":[{"award-number":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"]}]},{"name":"Key Research Projects of University in Henan Province, China","award":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"],"award-info":[{"award-number":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"]}]},{"name":"Programs for Science and Technology Development of Henan Province, China","award":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"],"award-info":[{"award-number":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"]}]},{"name":"Graduate Education Innovation and Quality Improvement Action Plan project","award":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"],"award-info":[{"award-number":["61976080","61771006","21A413002","20B510001","212102310298","222102210002","222102210004","222102220028","222102210088","SYLKC2022013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Online prediction of maneuvering target trajectory is one of the most popular research directions at present. Specifically, the primary factors balancing, between prediction accuracy and response time, will give the research substance. This paper presents an online trajectory prediction algorithm based on small sample chaotic time series (OTP-SSCT). First, we optimize in terms of data breadth. The dynamic split window is built according to the motion characteristics of the maneuvering target, thus realizing trajectory segmentation and constructing a small sample chaotic time series prediction set. Second, since fully considering the motion patterns of maneuvering targets, we introduce the spatiotemporal features into the particle swarm optimization (PSO) model identification algorithm, which improves the identification sensitivity of key trajectory data points. Furthermore, we propose a feedback optimization strategy of residual compensation to correct the trajectory prediction values to improve the prediction accuracy. For the initial value sensitivity problem of the PSO model identification algorithm, we propose a new initial population strategy, which improves the effectiveness of initial parameters on model identification. Through simulation experiment analysis, it is verified that the proposed OTP-SSCT algorithm achieves better prediction accuracy and faster response time.<\/jats:p>","DOI":"10.3390\/e24111668","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:27:34Z","timestamp":1668565654000},"page":"1668","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Online Tracking of Maneuvering Target Trajectory Based on Chaotic Time Series Prediction"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3413-9292","authenticated-orcid":false,"given":"Qian","family":"Wei","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7016-4412","authenticated-orcid":false,"given":"Peng","family":"Su","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2700-834X","authenticated-orcid":false,"given":"Lin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1109\/TITS.2017.2673778","article-title":"Sampling-based path planning for UAV collision avoidance","volume":"18","author":"Lin","year":"2017","journal-title":"IEEE Trans. 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