Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14165 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912435698401280 |
|---|---|
| author | Yang, Zhong Zhu, Zhengqiu Zhao, Yong Tian, Yonglin Fan, Changjun Guo, Runkang Lu, Wenhao Ge, Jingwei Chen, Bin Zhang, Yin Wu, Guohua Wang, Rui Eigner, Gyorgy Cheng, Guangquan Huang, Jincai Liu, Zhong Zhang, Jun Rudas, Imre J. Wang, Fei-Yue |
| author_facet | Yang, Zhong Zhu, Zhengqiu Zhao, Yong Tian, Yonglin Fan, Changjun Guo, Runkang Lu, Wenhao Ge, Jingwei Chen, Bin Zhang, Yin Wu, Guohua Wang, Rui Eigner, Gyorgy Cheng, Guangquan Huang, Jincai Liu, Zhong Zhang, Jun Rudas, Imre J. Wang, Fei-Yue |
| contents | 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 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as federated learning, blockchain, embodied intelligence, and large models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives Yang, Zhong Zhu, Zhengqiu Zhao, Yong Tian, Yonglin Fan, Changjun Guo, Runkang Lu, Wenhao Ge, Jingwei Chen, Bin Zhang, Yin Wu, Guohua Wang, Rui Eigner, Gyorgy Cheng, Guangquan Huang, Jincai Liu, Zhong Zhang, Jun Rudas, Imre J. Wang, Fei-Yue Signal Processing 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 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as federated learning, blockchain, embodied intelligence, and large models. |
| title | A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.14165 |