A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives
Zhong Yang, Zhengqiu Zhu, Yong Zhao, Yonglin Tian, Changjun Fan, Runkang Guo, Wenhao Lu, Jingwei Ge, Bin Chen, Yin Zhang, Guohua Wu, Rui Wang, Gyorgy Eigner, Guangquan Cheng, Jincai Huang, Zhong Liu, Jun Zhang, Imre J. Rudas, Fei-Yue Wang

TL;DR
This survey comprehensively reviews recent advances in underwater acoustic target tracking, emphasizing the impact of emerging technologies like deep learning and reinforcement learning, and proposes future research directions.
Contribution
It introduces a multidimensional taxonomy framework and systematically reviews over 180 publications from 2016 to 2025, highlighting technological shifts and challenges.
Findings
Deep learning significantly improves tracking accuracy.
Reinforcement learning enhances system adaptability.
Emerging technologies present new research opportunities.
Abstract
Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking has become a prominent research area of underwater communications and networking. Existing literature reviews often offer a narrow perspective or inadequately address the paradigm shifts driven by emerging technologies like deep learning and reinforcement learning. To address these gaps, this work presents a systematic survey of this field and introduces an innovative multidimensional taxonomy framework based on target scale, sensor perception modes, and sensor collaboration patterns. Within this framework, we comprehensively survey the literature (more than 180 publications) over the period…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Underwater Acoustics Research · Maritime Navigation and Safety
