{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T16:41:54Z","timestamp":1777135314643,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T00:00:00Z","timestamp":1615593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agency for Defense Development, Korea","award":["UD200005DD"],"award-info":[{"award-number":["UD200005DD"]}]},{"DOI":"10.13039\/501100016258","name":"Institute of Civil-Military Technology Cooperation","doi-asserted-by":"publisher","award":["18-SN-RB-01"],"award-info":[{"award-number":["18-SN-RB-01"]}],"id":[{"id":"10.13039\/501100016258","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton\u2013Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton\u2013Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.<\/jats:p>","DOI":"10.3390\/s21062033","type":"journal-article","created":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T23:52:06Z","timestamp":1615765926000},"page":"2033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Hybrid Newton\u2013Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7367-401X","authenticated-orcid":false,"given":"Raegeun","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Marine Science &amp; Convergence Engineering, Hanyang University ERICA, Ansan 15588, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1607-3629","authenticated-orcid":false,"given":"Yifang","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, 2nd Street, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jee Woong","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Marine Science &amp; Convergence Engineering, Hanyang University ERICA, Ansan 15588, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abraham, D.A. 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