水下声速场构建方法综述  

A review on the construction of underwater sound speed fields

作　　者：黄威 高凡[2] 王君婷 徐天河[2] HUANG Wei;GAO Fan;WANG Junting;XU Tianhe(Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100,China;Institute of Space Sciences,Shandong University,Weihai 264209,China)

机构地区：[1]中国海洋大学信息科学与工程学部,山东青岛266100 [2]山东大学空间科学研究院,山东威海264209

出　　处：《哈尔滨工程大学学报》2023年第11期2005-2017,共13页Journal of Harbin Engineering University

基　　金：崂山实验室科技创新项目(LSKJ202205100);山东省自然基金项目(ZR2023QF128);中国博士后基金项目(2022M722990);青岛市博士后基金项目(QDB-SH20220202061);中央高校基本科研业务费专项项目(202313036).

摘　　要：实时、精确地构建区域声速场是建设水下综合定位、导航、授时与通信体系的重要组成部分。本文梳理并分析了水下声速场构建领域的研究现状,总结了声速剖面直接测量方法与反演方法。声速反演法相比于直接测量法具有更高的便捷性和可接受的精度性能。然而,声速反演方法依赖于声呐观测数据,因此难以适用于无水下观测系统覆盖的地区,并且无法对未来时刻的声速分布进行预测。如何在无声场观测数据情况下,综合利用历史先验信息进行智能化、高精度地全海深声速场构建,弹性化为水下用户提供不同精度、实时性需求的声速分布估计服务,是未来声速场构建研究主流趋势。The real-time and accurate construction of regional sound velocity fields is a crucial component of build-ing a comprehensive underwater positioning,navigation,timing,and communication system.In this study,we re-viewed and analyzed the current research status of constructing underwater sound speed fields and summarized the direct measurement and inversion methods of sound velocity fields.The sound velocity inversion method has higher convenience and acceptable accuracy performance compared to the direct measurement method.Nevertheless,the inversion of sound velocity relies on sonar observation data,making its application difficult in areas without ocean observation systems.Moreover,the inversion method cannot predict future sound velocity distributions.The main-stream trend of future research on sound velocity field construction involves the comprehensive utilization of histori-cal prior information for precise construction of a whole-sea deep sound velocity field without sonar observation data and providing an elastic estimation service of sound velocity distribution with different precision and real-time requirements for underwater users.

关 键 词：水下声速场 声速剖面反演 射线声学理论 匹配场处理 正交经验函数分解 启发式算法 压缩感知 深度学习 神经网络 

分 类 号：P733.21[天文地球—物理海洋学]