{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T07:40:49Z","timestamp":1778744449613,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62073017"],"award-info":[{"award-number":["62073017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The multi-sensor artificial lateral line system (ALLS) can identify the flow-field\u2019s parameters to realize the closed-loop control of the underwater robotic fish. An inappropriate sensor placement of ALLS may result in inaccurate flow-field parametric identification. Therefore, this paper proposes a method to optimize the sensor placement configuration of the ALLS, which mainly included three algorithms, the feature importance algorithm based on mean and variance (MVF), the feature importance algorithm based on distance evaluation (DF), and the information redundancy (IR) algorithm. The optimal sensor placement performance selected by this method is verified by simulation. In addition, further experimental verification was conducted using the ALLS. Compared with the uniform sensor placement configuration mentioned in recent studies, the experimental results suggest that the optimal sensor placement method can achieve a more effective prediction of the flow-field parameters, therefore strengthening the underwater robotic fish\u2019s perception and control function.<\/jats:p>","DOI":"10.3390\/s21123980","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T14:16:04Z","timestamp":1623248164000},"page":"3980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Optimal Sensor Placement of the Artificial Lateral Line for Flow Parametric Identification"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2626-0287","authenticated-orcid":false,"given":"Dong","family":"Xu","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No.37 Xueyuan Road, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No.37 Xueyuan Road, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No.37 Xueyuan Road, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Beihang University, No.37 Xueyuan Road, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No.37 Xueyuan Road, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7376-6977","authenticated-orcid":false,"given":"Wei","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Reliability and System Engineering, Beihang University, No.37 Xueyuan Road, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"ref_1","unstructured":"Bleckmann, H. 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