{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:36:56Z","timestamp":1776400616274,"version":"3.51.2"},"reference-count":50,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Most existing target recognition methods based on high-resolution range profiles (HRRPs) use data from only one domain. However, the information contained in HRRP data from different domains is not exactly the same. Therefore, in the context of inverse synthetic aperture radar (ISAR), this paper proposes an advanced deep hybrid fusion network to utilize HRRP data from different domains for ship target recognition. First, the proposed network simultaneously processes time-domain HRRP and its corresponding time\u2013frequency (TF) spectrogram through two branches to obtain initial features from the two HRRP domains. Next, a feature alignment module is used to make the fused features more discriminative regarding the target. Finally, a decision fusion module is designed to further improve the model\u2019s prediction performance. We evaluated our approach using both simulated and measured data, encompassing ten different ship target types. Our experimental results on the simulated and measured datasets showed an improvement in recognition accuracy of at least 4.22% and 2.82%, respectively, compared to using single-domain data.<\/jats:p>","DOI":"10.3390\/rs16193701","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T12:02:20Z","timestamp":1728043340000},"page":"3701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Hybrid Fusion Network for Inverse Synthetic Aperture Radar Ship Target Recognition Using Multi-Domain High-Resolution Range Profile Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3832-1376","authenticated-orcid":false,"given":"Jie","family":"Deng","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2065-631X","authenticated-orcid":false,"given":"Fulin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.sigpro.2019.01.006","article-title":"Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition","volume":"158","author":"Du","year":"2019","journal-title":"Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9808","DOI":"10.1109\/JSEN.2023.3263591","article-title":"Measurement matrix optimization based on target prior information for radar imaging","volume":"23","author":"Chen","year":"2023","journal-title":"IEEE Sens. 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