A Review on Interception and Processing Techniques of Active Acoustic Radiated Signals from Small Underwater Targets

WANG Zhizhen; WANG Xiaoyan; AN Liang; CAO Hongli; QIAN Ronglai; YU Shiting; ZHU Chuanqi; ZHU Qixuan

doi:10.11993/j.issn.2096-3920.2026-0068

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2026 > Accepted Manuscript

WANG Zhizhen, WANG Xiaoyan, AN Liang, CAO Hongli, QIAN Ronglai, YU Shiting, ZHU Chuanqi, ZHU Qixuan. A Review on Interception and Processing Techniques of Active Acoustic Radiated Signals from Small Underwater Targets[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0068

Citation:

WANG Zhizhen, WANG Xiaoyan, AN Liang, CAO Hongli, QIAN Ronglai, YU Shiting, ZHU Chuanqi, ZHU Qixuan. A Review on Interception and Processing Techniques of Active Acoustic Radiated Signals from Small Underwater Targets[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0068

Citation:

WANG Zhizhen, WANG Xiaoyan, AN Liang, CAO Hongli, QIAN Ronglai, YU Shiting, ZHU Chuanqi, ZHU Qixuan. A Review on Interception and Processing Techniques of Active Acoustic Radiated Signals from Small Underwater Targets[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0068

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A Review on Interception and Processing Techniques of Active Acoustic Radiated Signals from Small Underwater Targets

doi: 10.11993/j.issn.2096-3920.2026-0068

Key Laboratory of Underwater Acoustic Signal Processing, Southeast University, Nanjing 210096, China

Received Date: 2026-04-08
Accepted Date: 2026-06-03
Rev Recd Date: 2026-06-02

Available Online: 2026-06-05

Abstract

Abstract

With the rapid development of underwater unmanned platforms and miniaturized equipment, the demand for detecting and recognizing small underwater targets has become increasingly significant. Such targets are characterized by low target strength, small size, and weak physical features, limiting the effectiveness of conventional active and passive detection methods. Therefore, the interception and processing of active acoustic radiation signals, such as navigation and communication signals, have emerged as one of the promising approaches for target detection. This paper reviews recent advances in interception and detection techniques for navigation pulse and communication signals of small underwater targets. The main sources and signal forms of relevant active acoustic signals are first analyzed, followed by a systematic review of non-cooperative signal interception, detection, and modulation recognition methods from a technological evolution perspective. Existing research shows an overall transition from traditional statistical and Manual feature extraction methods toward end-to-end deep learning-based processing approaches. Finally, this paper summarizes the existing research achievements in this field and provides an outlook on future research directions from three perspectives: interception and recognition of biomimetic underwater acoustic covert communication signals, physically consistent generative channel modeling, and specific emitter identification in non-cooperative scenarios.
- underwater small targets,
- underwater acoustic pulse signals,
- underwater acoustic communication signals,
- interception and detection,
- modulation recognition

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References(89)

References

[1]	Wibisono A, Piran M J, Song H K, et al. A survey on unmanned underwater vehicles: Challenges, enabling technologies, and future research directions[J]. Sensors, 2023, 23(17): 7321. doi: 10.3390/s23177321
[2]	Zhang B, Ji D, Liu S, et al. Autonomous underwater vehicle navigation: A review[J]. Ocean Engineering, 2023, 273: 113861. doi: 10.1016/j.oceaneng.2023.113861
[3]	Theocharidis T, Kavallieratou E. Underwater communication technologies: A review[J]. Telecommunication Systems, 2025, 88(2): 54. doi: 10.1007/s11235-025-01279-x
[4]	Picardi G, Borrelli C, SArti A, et al. A minimal metric for the characterization of acoustic noise emitted by underwater vehicles[J]. Sensors, 2020, 20(22): 6644. doi: 10.3390/s20226644
[5]	Diamant R, Lampe L. Low probability of detection for underwater acoustic communication: A review[J]. IEEE Access, 2018, 6: 19099-19112. doi: 10.1109/ACCESS.2018.2818110
[6]	张涛, 夏茂栋, 张佳宇, 等. 水下导航定位技术综述[J]. 全球定位系统, 2022, 47(4): 1-16. doi: 10.12265/j.gnss.2022094 Zhang T, Xia M D, Zhang J Y, et al. A review of underwater navigation and positioning technologies[J]. GNSS World of China, 2022, 47(4): 1-16. doi: 10.12265/j.gnss.2022094
[7]	Ma T, Zeng S, Zhou Y. Communication and networking technologies for unmanned underwater vehicle swarms: A survey[J]. IEEE Open Journal of the Communications Society, 2025, 7: 734-747. doi: 10.1109/ojcoms.2025.3643165
[8]	商志刚, 曲星昊, 马璐, 等. 水声通信定位一体化研究综述[J]. 声学学报, 2026, 51(2): 465-477. Shang Z G, Qu X H, Ma L, et al. A review of research on underwater acoustic integrated communication and localization[J]. Acta Acustica, 2026, 51(2): 465-477.
[9]	Alexandris C, Papageorgas P, Piromalis D. Positioning systems for unmanned underwater vehicles: A comprehensive review[J]. Applied Sciences, 2024, 14(21): 9671. doi: 10.3390/app14219671
[10]	查峰, 张全超, 卜浩宇, 等. 单信标水声定位方法及其关键技术分析[J/OL]. (2026-04-01)声学技术, DOI: 10.16300/j.cnki.1000-3630.25061202. Zha F, Zhang Q C, Bu H Y, et al. A review of single-beacon methods and key technologies for underwater acoustic positioning[J/OL]. (2026-04-01) Technical Acoustics, DOI: 10.16300/j.cnki.1000-3630.25061202.
[11]	高清泽. 基于CW脉冲的水声信号编码方式的设计与工程实现[J]. 中国应急救援, 2020(1): 26-29. doi: 10.3969/j.issn.1673-5579.2020.01.007 Gao Q Z. Design and engineering realization of underwater acoustic signal coding based on cw pulse[J]. China Emergency Rescue, 2020(1): 26-29. doi: 10.3969/j.issn.1673-5579.2020.01.007
[12]	Han Y, Zheng C, Sun D. Signal design for underwater acoustic positioning systems based on orthogonal waveforms[J]. Ocean Engineering, 2016, 117: 15-21. doi: 10.1016/j.oceaneng.2016.03.017
[13]	庞永丽, 郭亚静, 王黎明. 基于混沌的水声定位信号设计及性能分析[J]. 中国测试, 2017, 43(10): 134-138. Pang Y L, Guo Y J, Wang L M. Design and performance analysis of underwater acoustic positioning signal based on chaos[J]. CHINA MEASUREMENT & TEST, 2017, 43(10): 134-138.
[14]	郭亚静, 王黎明, 王琳, 等. 一种基于Logistic映射的水声混沌信号测距方法[J]. 科学技术与工程, 2017, 17(5): 10-14,46. Guo Y J, Wang L M, Wang L, et al. An acoustic chaotic signal ranging method based on the Logistic mapping[J]. Science Technology and Engineering, 2017, 17(5): 10-14,46.
[15]	邱念庭, 陈胜利, 袁飞, 等. 组合chirp信号在水下声基站定位信号冲突避免中的应用研究[J]. 南京大学学报 (自然科学版), 2017, 53(4): 667-674. Qiu N T, Chen S L, YUAN F, et al. Application of combined chirp signal in collision avoidance of underwater acoustic station positioning signal[J]. Journal of Nanjing University(Natural Science), 2017, 53(4): 667-674.
[16]	杨健敏, 王佳惠, 乔钢, 等. 水声通信及网络技术综述[J]. 电子与信息学报, 2024, 46(1): 1-21. doi: 10.11999/JEIT230424 Yang J M, Wang J H, Qiao G, et al. Review of underwater acoustic communication and network technology[J]. Journal of Electronics & Information Technology, 2024, 46(1): 1-21. doi: 10.11999/JEIT230424
[17]	Haziq M, Phung Q V, Lachowicz S, et al. Modulation techniques for underwater acoustic communication: A comprehensive survey[J]. IEEE Access, 2025, 13: 150715-150755. doi: 10.1109/ACCESS.2025.3601799
[18]	Qiao G, Bilal M, Liu S, et al. Biologically inspired covert underwater acoustic communication-a review[J]. Physical Communication, 2018, 30: 107-114. doi: 10.1016/j.phycom.2018.07.007
[19]	Urkowitz H. Energy detection of unknown deterministic signals[J]. Proceedings of the IEEE, 1967, 55(4): 523-531. doi: 10.1109/PROC.1967.5573
[20]	Kaiser J F. On a simple algorithm to calculate the 'energy' of a signal[C]//International Conference on Acoustics, Speech, and Signal Processing, 1990: 381-384.
[21]	Dunn R B, Quatieri T F, Kaiser J F. Detection of transient signals using the energy operator[C]//1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993, 3: 145-148.
[22]	王晓燕, 方世良, 朱志峰. 非合作水声脉冲信号的熵检测方法[C]//泛在信息社会中的声学——中国声学学会2010年全国会员代表大会暨学术会议论文集. 2010.
[23]	向大威, 许伟杰, 景永刚. 水声脉冲信号检测的新方法[J]. 声学技术, 2012(1): 19-23. Xiang D W, Xu W J, Jing Y G. New method for underwater acoustic pulse signal detection[J]. Technical Acoustics, 2012(1): 19-23.
[24]	Wei K, Fang S, Tao J. Variable scale relative entropy detection for non-cooperative underwater acoustic pulse signals[J]. IEEE Access, 2020, 8: 66131-66138. doi: 10.1109/ACCESS.2020.2985051
[25]	夏文杰, 黎鑫, 曹伟浩, 等. 基于分数阶功率谱熵的未知水声脉冲信号检测方法[J]. 声学技术, 2021, 40(3): 415-421. doi: 10.16300/j.cnki.1000-3630.2021.03.019 Xia W J, Li X, Cao W H, et al. Detection of unknown underwater acoustic pulse signal based on fractional power spectrum entropy[J]. Technical Acoustics, 2021, 40(3): 415-421. doi: 10.16300/j.cnki.1000-3630.2021.03.019
[26]	Wei L, Fang T, Wang B. A non-cooperative underwater acoustic pulse signal detection method based on the improved fractional Fourier transform power spectral entropy[C]//Journal of Physics: Conference Series, 2025, 2991(1): 012015.
[27]	查淞元, 刘雨东. 基于频谱熵的水声脉冲信号检测方法[J]. 舰船科学技术, 2025, 47(18): 142-148. doi: 10.3404/j.issn.1672-7649.2025.18.023 Zha S Y, Liu Y D. Detection method of underwater acoustic pulse signal based on spectral entropy[J]. Ship Science and Technology, 2025, 47(18): 142-148. doi: 10.3404/j.issn.1672-7649.2025.18.023
[28]	Ma L, Fan C, Sun W, et al. Comparison of detection methods for noncooperative underwater acoustic DSSS signals[C]//2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2017: 1-5.
[29]	王大宇, 王志欣, 张光普. 基于改进谱减算法的水声通信信号检测方法[J]. 应用科技, 2020, 47(3): 69-73. doi: 10.11991/yykj.202005006 Wang D Y, Wang Z X, Zhang G P. Detection method of underwater acoustic communications signal based on improved spectral subtraction algorithm[J]. Applied Science and Technology, 2020, 47(3): 69-73. doi: 10.11991/yykj.202005006
[30]	Zhang H, Wang H, Yan Y, et al. Non-cooperative underwater acoustic communication signal detection method based on sliding cepstrum[C]//2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2022: 1-5.
[31]	王晓燕, 方世良, 朱志峰. 基于ST-FRFT的非合作水声脉冲信号检测方法[J]. 信号处理, 2011, 27(8): 1271-1278. doi: 10.3969/j.issn.1003-0530.2011.08.026 Wang X Y, Fang S L, Zhu Z F. Detection of non-cooperative underwater acoustic pulse signal based on ST-FRFT[J]. Signal Processing, 2011, 27(8): 1271-1278. doi: 10.3969/j.issn.1003-0530.2011.08.026
[32]	徐景峰, 韩树平, 舒象兰, 等. 非平稳噪声背景下瞬态声脉冲检测新方法[J]. 声学学报, 2016, 41(3): 362-370. Xu J F, Han S P, Shu X L, et al. A new method of transient acoustic pulse detection under the nonstationary noise background[J]. Acta Acustica, 2016, 41(3): 362-370.
[33]	Cheng K, Wang F Y, Du S P. Passive detection method based on non-cooperative underwater acoustic pulse signal[C]//2022 5th International Conference on Information Communication and Signal Processing (ICICSP), 2022: 584-588.
[34]	Wei K, Fang S, Tao J. Enhanced near-field interference suppression scheme for the non-cooperative underwater acoustic pulse detection of the towed linear array[J]. Journal of Marine Science and Engineering, 2022, 10(2): 250. doi: 10.3390/jmse10020250
[35]	王洋, 沈同圣, 汪涛, 等. 基于自适应形态学滤波与霍夫变换的水声通信信号检测方法[J]. 通信学报, 2025, 46(7): 29-44. doi: 10.11959/j.issn.1000-436x.2025117 Wang Y, Shen T S, Wang T, et al. Detection method for underwater acoustic communication signals based on adaptive morphological filtering and Hough transform[J]. Journal on Communications, 2025, 46(7): 29-44. doi: 10.11959/j.issn.1000-436x.2025117
[36]	Cheng L, Liu Y, Wei J, et al. Array-based faster R-CNN for underwater acoustic communication signal detection[C]//2024 IEEE 8th International Conference on Vision, Image and Signal Processing(ICVISP), 2024: 1-5.
[37]	成乐, 刘悦, 胡正良, 等. 基于阵列的神经网络水声通信信号多参数联合估计算法[J]. 通信学报, 2025, 46(1): 67-78. Cheng Y, Liu Y, Hu Z L, et al. Array-based neural network algorithm for multi-parameter joint estimation of underwater acoustic communication signals[J]. Journal on Communications, 2025, 46(1): 67-78.
[38]	Zhao H, Wang B, Zhang L, et al. MMFFNet: Multi-node fusion network for passive fusion detection of underwater acoustic communication signals[J]. IEEE Communications Letters, 2025, 29(7): 1525-1529. doi: 10.1109/LCOMM.2025.3566281
[39]	Hemminger T L, Pao Y H. Detection and classification of underwater acoustic transients using neural networks[J]. IEEE Transactions on Neural Networks, 1994, 5(5): 712-718. doi: 10.1109/72.317723
[40]	Li Y, Ma X, Liu Y, et al. Application of deep object detection in underwater acoustic pulse interception[C]//Oceans 2019-Marseille, 2019: 1-7.
[41]	Zhou L, Qiu J, Zhang J, et al. YOLO-based detection method for complex underwater acoustic pulse signals[C]//2025 8th International Conference on Computer Information Science and Application Technology(CISAT), 2025: 1084-1088.
[42]	Li Y, Wang B, Shao G, et al. Blind detection of underwater acoustic communication signals based on deep learning[J]. IEEE Access, 2020, 8: 204114-204131. doi: 10.1109/ACCESS.2020.3036883
[43]	Li M, Li J, Feng H. Detection and recognition of underwater acoustic communication signal under ocean background noise[J]. IEEE Access, 2024, 12: 149432-149446. doi: 10.1109/access.2024.3476494
[44]	Uvaydov D, Unal D, Enhos K, et al. Sonair: Real-time deep learning for underwater acoustic spectrum sensing and classification[C]//2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things(DCOSS-IoT), 2023: 9-16.
[45]	Li Y, Zhang L, Wang K, et al. Underwater acoustic intelligent spectrum sensing with multimodal data fusion: An Mul-YOLO approach[J]. Future Generation Computer Systems, 2025, 173: 107880. doi: 10.1016/j.future.2025.107880
[46]	Wu Z, Yang T C, Liu Z, et al. Modulation detection of underwater acoustic communication signals through cyclostationary analysis[C]//Milcom 2012-2012 IEEE Military Communications Conference, 2012: 1-6.
[47]	Li X, Han Q, Liu Z, et al. Novel modulation detection scheme for underwater acoustic communication signal through short-time detailed cyclostationary features[C]//2014 IEEE Wireless Communications and Networking Conference(WCNC), 2014: 624-629.
[48]	Sanderson J, Li X, Liu Z, et al. Hierarchical blind modulation classification for underwater acoustic communication signal via cyclostationary and maximal likelihood analysis[C]//MILCOM 2013-2013 IEEE Military Communications Conference, 2013: 29-34.
[49]	Zhang X L, Anwar S, Junejo N U R, et al. Application of cyclic cumulant in recognition of underwater communication system[C]//2017 IEEE International Conference on Signal Processing, Communications and Computing(ICSPCC), 2017: 1-4.
[50]	Nie L, Li C, Wang H, et al. An improved cumulant-based modulation classification method for underwater acoustic applications[C]//2020 IEEE 5th International Conference on Signal and Image Processing(ICSIP), 2020: 547-551.
[51]	Li J Q, Anwar S, Zhang X L, et al. Autocorrelation based modulation recognition of PSK signals for OFDM in underwater acoustics communication[C]//2018 15th International Bhurban Conference on Applied Sciences and Technology(IBCAST), 2018: 751-756.
[52]	Stanescu D, Digulescu A, Ioana C, et al. Modulation recognition of underwater acoustic communication signals based on phase diagram entropy[C]//OCEANS 2022, Hampton Roads, 2022: 1-7.
[53]	江伟华, 曹秀岭, 童峰. 采用支持向量机的水声通信信号调制识别方法[J]. 厦门大学学报: 自然科学版, 2015, 54(4): 534-539. doi: 10.6043/j.issn.0438-0479.2015.04.017 Jiang W H, Cao X L, Tong F. Modulation recognition method of underwater acoustic communication signal using SVM[J]. Journal of Xiamen University: Natural Science, 2015, 54(4): 534-539. doi: 10.6043/j.issn.0438-0479.2015.04.017
[54]	Zhao Z, Wang S, Zhang W, et al. A novel automatic modulation classification method based on Stockwell-transform and energy entropy for underwater acoustic signals[C]//2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2016: 1-6.
[55]	Cheng E, Yan J, Sun H, et al. Research on MPSK modulation classification of underwater acoustic communication signals[C]//2016 IEEE/OES China Ocean Acoustics (COA), 2016: 1-5.
[56]	Jiang W, Tong F, Dong Y, et al. Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis[J]. Applied Acoustics, 2018, 138: 209-215. doi: 10.1016/j.apacoust.2018.03.033
[57]	Zhang L, Zhu H, Sun W, et al. Performance analysis of underwater acoustic automatic modulation classification system with machine learning[C]//2021 IEEE 21st International Conference on Communication Technology (ICCT), 2021: 1310-1314.
[58]	Fang T, Wang Q, Zhang L, et al. Modulation mode recognition method of non-cooperative underwater acoustic communication signal based on spectral peak feature extraction and random forest[J]. Remote Sensing, 2022, 14(7): 1603.
[59]	Wang M, Zhu Z, Qian G. Modulation signal recognition of underwater acoustic communication based on archimedes optimization algorithm and random forest[J]. Sensors, 2023, 23(5): 2764.
[60]	王洋, 沈同圣, 汪涛, 等. 融合多特征的水声通信信号调制识别方法[J]. 声学学报, 2025, 50(5): 1338-1348. Wang Y, Shen T S, Wang T, et al. Modulation classification method based on multi-feature fusion for underwater acoustic communication signals[J]. Acta Acustica, 2025, 50(5): 1338-1348.
[61]	Jiang J, Qiao F, Li Y, et al. Recognition method for the bionic camouflage click communication trains modulated by time delay difference[J]. The Journal of the Acoustical Society of America, 2022, 152(1): 491-500.
[62]	Jiang J, Yao Z, Li Z C, et al. Recognition method for the bionic camouflage cetacean whistle modulated by CPMFSK signals[J]. Applied Acoustics, 2023, 207: 109326. doi: 10.1016/j.apacoust.2023.109326
[63]	Yao Q, Jiang J, Chen G, et al. Recognition method for underwater imitation whistle communication signals by slope distribution[J]. Applied Acoustics, 2023, 211: 109531. doi: 10.1016/j.apacoust.2023.109531
[64]	刘亚男, 刘凇佐, 方涛, 等. 仿海豚哨声频率调制水声通信信号自动识别[J]. 声学学报, 2026, 51(1): 310-321. Liu Y N, Liu S Z, Fang T, et al. Automatic recognition of frequency-modulated bionic underwater acoustic communication signals mimicking dolphin whistles[J]. Acta Acustica, 2026, 51(1): 310-321.
[65]	Ding L D, Wang S L, Zhang W. Modulation classification of underwater acoustic communication signals based on deep learning[C]//2018 OCEANS-MTS/IEEE Kobe Techno-Oceans(OTO), 2018: 1-4.
[66]	Wang Y, Zhang H, Xu L, et al. Adoption of hybrid time series neural network in the underwater acoustic signal modulation identification[J]. Journal of the Franklin Institute, 2020, 357(18): 13906-13922.
[67]	Zhang W, Yang X, Leng C, et al. Modulation recognition of underwater acoustic signals using deep hybrid neural networks[J]. IEEE Transactions on Wireless Communications, 2022, 21(8): 5977-5988. doi: 10.1109/TWC.2022.3144608
[68]	Yao X, Yang H, Sheng M. Automatic modulation classification for underwater acoustic communication signals based on deep complex networks[J]. Entropy, 2023, 25(2): 318. doi: 10.3390/e25020318
[69]	黄乐, 夏志军, 周胜增, 等. 基于深度学习的水声通信信号调制类型识别[J]. 舰船科学技术, 2024, 46(9): 117-124. doi: 10.3404/j.issn.1672-7649.2024.09.020 Huang L, Xia Z J, Zhou S Z, et al. Underwater acoustic communication signal recognition based on deep learning[J]. Ship Science and Technology, 2024, 46(9): 117-124. doi: 10.3404/j.issn.1672-7649.2024.09.020
[70]	Wang X, Liu J, Han G, et al. End-to-end modulation recognition in underwater acoustic communications using temporal large kernel convolution with gated channel mixer[J]. IEEE Transactions on Vehicular Technology, 2024, 73(10): 15076-15086. doi: 10.1109/TVT.2024.3406515
[71]	巩文静, 李宇, 丁飞龙, 等. 基于注意力残差网络的非合作水声通信信号自动调制识别[J]. 声学学报, 2026, 51(1): 298-309. doi: 10.12395/0371-0025.2025139 Gong W J, Li Y, Ding F L, et al. Automatic modulation recognition of non-cooperative underwater acoustic communication signals based on attention residual networks[J]. Acta Acustica, 2026, 51(1): 298-309. doi: 10.12395/0371-0025.2025139
[72]	王彬, 王海旺, 李勇斌. 脉冲噪声环境下水声通信信号调制识别方法[J]. 信号处理, 2020, 36(12): 2107-2115. doi: 10.16798/j.issn.1003-0530.2020.12.017 Wang B, Wang H W, Li Y B. Modulation recognition method of underwater acoustic communication signals in impulsive noise environment[J]. Journal of Signal Processing, 2020, 36(12): 2107-2115. doi: 10.16798/j.issn.1003-0530.2020.12.017
[73]	Kong X Y, Tao J, Wu Y, et al. Channel replay aided modulation classification of underwater acoustic communication signals[C]//2021 IEEE/CIC International Conference on Communications in China(ICCC Workshops), 2021: 228-232.
[74]	Wang H, Wang B, Wang R, et al. Modulation recognition method for underwater acoustic communication signal based on relation network under small sample set[C]//2021 IEEE 21st International Conference on Communication Technology(ICCT), 2021: 1287-1291.
[75]	Wang H, Wang B, Li Y. IAFNet: Few-shot learning for modulation recognition in underwater impulsive noise[J]. IEEE Communications Letters, 2022, 26(5): 1047-1051. doi: 10.1109/LCOMM.2022.3151790
[76]	Xu Z, Wu X, Gao D, et al. Blind modulation recognition of UWA signals with semi-supervised learning[C]//2022 IEEE International Conference on Signal Processing, Communications and Computing(ICSPCC), 2022: 1-5.
[77]	瞿逢重, 朱江, 涂星滨, 等. 基于GRU和ResNet的短时水声通信信号调制识别[J]. 信号处理, 2023, 39(10): 1793-1804. Qu F Z, Zhu J, Tu X B, et al. Automatic modulation recognition of short-time underwater acoustic communication signal based on GRU and ResNet[J]. Journal of Signal Processing, 2023, 39(10): 1793-1804.
[78]	Cao M, Chen Q, Tang J, et al. Data augmentation and time–frequency joint attention for underwater acoustic communication modulation classification[J]. Journal of Marine Science and Engineering, 2026, 14(2): 172. doi: 10.3390/jmse14020172
[79]	Huang W, Fan R, Su Y. Diffusion model-based data augmentation with mel spectrogram for automatic modulation recognition in underwater acoustic communication [J/OL]. IEEE Internet of Things Journal, 2026. [2026-06-03]. DOI: 10.1109/JIOT.2026.3673750.
[80]	Li J, Jia Q, Cui X, et al. Automatic modulation recognition of underwater acoustic signals using a two-stream transformer[J]. IEEE Internet of Things Journal, 2024, 11(10): 18839-18851. doi: 10.1109/JIOT.2024.3367852
[81]	Lyu Y, Cheng X, Wang Y. Automatic modulation identification for underwater acoustic signals based on the space–time neural network[J]. Frontiers in Marine Science, 2024, 11: 1334134. doi: 10.3389/fmars.2024.1334134
[82]	Wang Y, Shen T, Wang T, et al. Modulation recognition for underwater acoustic communication based on hybrid neural network and feature fusion[J]. Applied Acoustics, 2024, 225: 110185. doi: 10.1016/j.apacoust.2024.110185
[83]	Wang J, Huang Z, Shi W, et al. One2ThreeNet: An automatic microscale-based modulation recognition method for underwater acoustic communication systems[J]. IEEE Transactions on Wireless Communications, 2024, 23(8): 10287-10300. doi: 10.1109/TWC.2024.3371226
[84]	Jiang Z, Zhang J, Wang T, et al. Modulation recognition of underwater acoustic communication signals based on neural architecture search[J]. Applied Acoustics, 2024, 225: 110155. doi: 10.1016/j.apacoust.2024.110155
[85]	Wang X, Tu Y, Liu J, et al. Edge-enabled modulation classification in internet of underwater things based on network pruning and ensemble learning[J]. IEEE Internet of Things Journal, 2023, 11(8): 13608-13621. doi: 10.1109/jiot.2023.3338147
[86]	Yao Q, Jiang J, Yu X, et al. Recognition method for biomimetic camouflage communication signal imitating cetacean click in underwater multipath channels[J]. Applied Acoustics, 2024, 222: 110039. doi: 10.1016/j.apacoust.2024.110039
[87]	Yao Q, Jiang J, Yu X, et al. Recognition method for underwater communication signals that mimic dolphin whistles using phase-shifting modulation[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1754-1764. doi: 10.1631/FITEE.2400572
[88]	杨硕硕, 王彬, 郑娄, 等. 融合多模态对比学习的仿嘀嗒声水声仿生通信信号识别方法[J]. 信息工程大学学报, 2026, 27(1): 19-26,34. Yang S S, Wang B, Zheng L, et al. Recognition of hydroacoustic biomimetic camouflage click communication train using fused multimodal contrastive learning[J]. Journal of Information Engineering University, 2026, 27(1): 19-26,34.
[89]	Li K, Chitre M. Diffusion-based surrogate model for time-varying underwater acoustic channels[EB/OL]. (2025-12-12)[2026-06-03]. https://arxiv.org/abs/2511.18078, arXiv: 2511.18078.