本文基于科学引文索引，检索了 1108 篇与“潜艇声呐”主题紧密相关的国外文献作为数据源。采用 LDA 主题建模技术，精确识别了六大核心主题：声呐技术与应用、声呐与机器人技术、声呐探测系统与应用、水下通信与导航、反潜作战领域的声呐技术、声呐与人工智能。基于这些主题，对原始数据进行了系统性的分类汇总与深入分析。本研究旨在通过深度剖析当前国外文献的研究焦点与趋势，为相关领域的研究者提供快速掌握领域发展脉络的途径。此举不仅有助于我国研究人员精准定位未来研究方向，更将积极促进我国潜艇声呐技术的进步与海洋强国战略的发展。
This study utilized the Science Citation Index to retrieve 1108 international publications closely related to the topic of “submarine sonar” as its data source. By employing Latent Dirichlet Allocation (LDA) topic modeling techniques, six core themes were precisely identified: sonar technology and applications, sonar and robotics, sonar detection systems and applications, underwater communication and navigation, sonar technology in anti-submarine warfare, and sonar and artificial intelligence. Based on these themes, we conducted a systematic classification, summarization, and in-depth analysis of the original data. This research aims to provide researchers in related fields with a means to quickly grasp the developmental trajectory of the field by thoroughly analyzing the current research focus and trends in international literature. This effort not only helps Chinese researchers accurately pinpoint future research directions but also actively promotes the advancement of China's submarine sonar technology and the development of its maritime power strategy.
2024,46(18): 184-189 收稿日期：2024-4-13
DOI：10.3404/j.issn.1672-7649.2024.18.034
分类号：U666.7
基金项目：黑龙江省本科高校外语教育改革创新项目（HWX202202-B）
作者简介：崔丹(1974-),女,博士,教授,研究方向为语言学/翻译
参考文献：
[1] 光明日报. 为潜艇声呐贡献一切[EB/OL]. [2024-08-22]. https://www.gmw.cn/01gmrb/1999-11/15/ GB/GM%5E18241%5E2%5EGM2-1504.HTM.
[2] BLEI D M. Latent Dirichlet Allocation[J]. Journal of machine Learning research, 2003: 993–1022.
[3] YU Y, MAYKUT G A, ROTHROCK D A. Changes in the thickness distribution of Arctic sea ice between 1958—1970 and 1993–1997[J]. Journal of Geophysical Research: Oceans, 2004: 109.
[4] KRISHNA KUMAR G V, PADMANABAM M, SREE M B et al. Simulation of Radiated Noise signature of a marine vessel[C]//2015 IEEE Underwater Technology (UT). Chennai, India. 2015: 1–4.
[5] FRICKE M B, ROLFES R. Investigation of sonar transponders for offshore wind farms: Modeling approach, experimental setup, and results[J]. The Journal of the Acoustical Society of America, 2013, 134(5): 3536-3545.
[6] WHITCOMB L L, JAKUBA M V, KINSEY J C et al. Navigation and control of the Nereus hybrid underwater vehicle for global ocean science to 10, 903 m depth: Preliminary results[C]//2010 IEEE International Conference on Robotics and Automation. AK, USA. 2010: 594–600.
[7] HAGEN P E, STORKERSEN N, MARTHINSEN B E et al. Military operations with HUGIN AUVs: lessons learned and the way ahead[C]//Europe Oceans 2005. Brest, France. 2005: 810–813.
[8] MATTHEWS A D, MONTGOMERY T C, COOK D A et al. 12.75" Synthetic Aperture Sonar (SAS), High Resolution and Automatic Target Recognition[C]//OCEANS 2006. Boston, MA, USA. 2006: 1–7.
[9] HILL P, DE DECKKER P, EXON N. Geomorphology and evolution of the gigantic Murray canyons on the Australian southern margin[J]. Australian Journal of Earth Sciences, 2005, 52(1): 117-136.
[10] SCHLÜTER M, SAUTER E J, ANDERSEN C E et al. Spatial distribution and budget for submarine groundwater discharge in Eckernförde Bay (Western Baltic Sea)[J]. Limnology and Oceanography, 2004, 49(1): 157-167.
[11] LAJEUNESSE P, ST-ONGE G. The subglacial origin of the Lake Agassiz–Ojibway final outburst flood[J]. Nature Geoscience, 2008, 1(3): 184-188.
[12] GHAFOOR H, NOH Y. An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture[J]. IEEE Access, 2019, 7: 98841-98853.
[13] VIAL P, PALOMERAS N, SOLÀ J et al. Underwater Pose SLAM using GMM scan matching for a mechanical profiling sonar[J]. Journal of Field Robotics, 2024, 41(3): 511-538.
[14] RAHMAN S, LI A Q, REKLEITIS I. SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Macau, China. 2019: 1861–1868.
[15] RICKS R, GRIMMETT D, WAKAYAMA C. Passive acoustic tracking for cueing a multistatic active acoustic tracking system[C]//2012 Oceans - Yeosu. Yeosu, Korea (South). 2012: 1–7.
[16] CHU P C, AMEZAGA G, GOTTSHALL E L et al. Assessment of ocean prediction model for naval operations using acoustic preset[C]//Proceedings of OCEANS 2005 MTS/IEEE. Washington, DC, USA. 2005: 986–995.
[17] DALE J, GALUSHA A, KELLER J et al. Evaluation of image features for discriminating targets from false positives in synthetic aperture sonar imagery[C]//Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV 11012. Baltimore, MD, United States. 2019: 110120A
[18] LI J H, PARK D, KI G. Autonomous swimming technology for an AUV operating in the underwater jacket structure environment[J]. International Journal of Naval Architecture and Ocean Engineering, 2019, 11(2): 679-687.
[19] HENTHORN R, CARESS D W, THOMAS H et al. High-resolution multibeam and subbottom surveys of submarine canyons, DeepSea fan channels, and gas seeps using the MBARI mapping AUV[C]//OCEANS 2006. Boston, MA, USA. 2006: 1–6.
