{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T14:32:51Z","timestamp":1771338771596,"version":"3.50.1"},"reference-count":16,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep learning (DL)-based modulation recognition methods of underwater acoustic communication signals are mostly applied to a single hydrophone reception scenario. In this paper, we propose a novel end-to-end multihydrophone fusion network (MHFNet) for multisensory reception scenarios. MHFNet consists of a feature extraction module and a fusion module. The feature extraction module extracts the features of the signals received by the multiple hydrophones. Then, through the neural network, the fusion module fuses and classifies the features of the multiple signals. MHFNet takes full advantage of neural networks and multihydrophone reception to effectively fuse signal features for realizing improved modulation recognition performance. Experimental results on simulation and practical data show that MHFNet is superior to other fusion methods. The classification accuracy is improved by about 16%.<\/jats:p>","DOI":"10.3390\/s22093214","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:45:21Z","timestamp":1650761121000},"page":"3214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multihydrophone Fusion Network for Modulation Recognition"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0001-8580","authenticated-orcid":false,"given":"Haiwang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Systems Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Systems Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Lulu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Systems Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Qiang","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Systems Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ansari, N., and Su, W. 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Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yao, X., Yang, H., and Li, Y. (2019, January 27\u201331). Modulation Identification of Underwater Acoustic Communications Signals Based on Generative Adversarial Networks. Proceedings of the 2019 OCEANS, Seattle, WA, USA.","DOI":"10.1109\/OCEANSE.2019.8867125"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/LWC.2021.3078878","article-title":"Deep Learning Based Modulation Recognition With Multi-Cue Fusion","volume":"10","author":"Wang","year":"2021","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_10","unstructured":"Ding, L., Wang, S., and Zhang, W. (2018, January 28\u201331). Modulation Classification of Underwater Acoustic Communication Signals Based on Deep Learning. 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