{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T15:03:38Z","timestamp":1778425418647,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:00:00Z","timestamp":1725494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62388102"],"award-info":[{"award-number":["62388102"]}]},{"name":"National Natural Science Foundation of China","award":["62101583"],"award-info":[{"award-number":["62101583"]}]},{"name":"National Natural Science Foundation of China","award":["61871392"],"award-info":[{"award-number":["61871392"]}]},{"name":"National Natural Science Foundation of China","award":["tsqn202211246"],"award-info":[{"award-number":["tsqn202211246"]}]},{"name":"Taishan Scholars Program","award":["62388102"],"award-info":[{"award-number":["62388102"]}]},{"name":"Taishan Scholars Program","award":["62101583"],"award-info":[{"award-number":["62101583"]}]},{"name":"Taishan Scholars Program","award":["61871392"],"award-info":[{"award-number":["61871392"]}]},{"name":"Taishan Scholars Program","award":["tsqn202211246"],"award-info":[{"award-number":["tsqn202211246"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper constructs a nonlinear iterative filtering framework based on a neural network prediction model. It uses recurrent neural networks (RNNs) to achieve accurate regression of complex maneuvering target dynamic models and integrates them into the nonlinear iterative filtering system via Unscented Transformation (UT). In constructing the neural network prediction model, the Temporal Convolutional Network (TCN) modules that capture long-term dependencies and the Long Short-Term Memory (LSTM) modules that selectively forget non-essential information were utilized to achieve accurate regression of the maneuvering models. When embedding the neural network prediction model, this paper proposes a method for extracting Sigma points using the UT transformation by \u2018unfolding\u2019 multi-sequence vectors and explores design techniques for the time sliding window length of recurrent neural networks. Ultimately, an intelligent tracking algorithm based on unscented filtering, called TCN-LSTM-UKF, was developed, effectively addressing the difficulties of constructing models and transition delays under high-maneuvering conditions and significantly improving the tracking performance of highly maneuvering targets.<\/jats:p>","DOI":"10.3390\/rs16173301","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T06:44:29Z","timestamp":1725518669000},"page":"3301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Intelligent Tracking Method for Aerial Maneuvering Target Based on Unscented Kalman Filter"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunlong","family":"Dong","sequence":"first","affiliation":[{"name":"Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqi","family":"Li","sequence":"additional","affiliation":[{"name":"Yantai Research Institute, Harbin Engineering University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxue","family":"Li","sequence":"additional","affiliation":[{"name":"Yantai Research Institute, Harbin Engineering University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6302-4219","authenticated-orcid":false,"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xue","sequence":"additional","affiliation":[{"name":"Yantai Research Institute, Harbin Engineering University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Q., Li, R., Ji, K., and Dai, W. 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