Two-Dimensional Deconvolved Conventional Beamforming Based on Non-Stationary Signal Demodulation

FAN Xiaomeng; LIU Zhen; LI Mei; YE Tianming

doi:10.11993/j.issn.2096-3920.2026-0064

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

FAN Xiaomeng, LIU Zhen, LI Mei, YE Tianming. Two-Dimensional Deconvolved Conventional Beamforming Based on Non-Stationary Signal Demodulation[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0064

Citation:

FAN Xiaomeng, LIU Zhen, LI Mei, YE Tianming. Two-Dimensional Deconvolved Conventional Beamforming Based on Non-Stationary Signal Demodulation[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0064

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Two-Dimensional Deconvolved Conventional Beamforming Based on Non-Stationary Signal Demodulation

doi: 10.11993/j.issn.2096-3920.2026-0064

Shanghai Marine Electronic Equipment Research Institute, Shanghai 201108, China

Received Date: 2026-03-31
Accepted Date: 2026-05-15
Rev Recd Date: 2026-05-13

Available Online: 2026-05-20

Abstract

Abstract

Underwater weak target detection is a core topic in underwater acoustics signal processing, yet it still faces numerous challenges such as complex environments, numerous interfering targets, weak echoes, and poor image quality. Frogmen are typical weak underwater intrusion targets. To address the difficulty in their detection, this paper proposes a two-dimensional deconvolved conventional beamforming based on non-stationary signal demodulation. This method utilizes two-dimensional deconvolved conventional beamforming method to obtain high-resolution target azimuth-range images. By combining peak detection on these high-resolution images, the time-domain waveform of suspected target echoes is precisely located. Subsequently, the time-domain waveforms of suspected echoes are demodulated and the spectral kurtosis index is calculated. Leveraging the difference in spectral kurtosis between genuine target echoes and clutter, weights are assigned to the two-dimensional deconvolved conventional beamforming images to achieve enhancement of weak target echoes and suppression of clutter, ultimately yielding high –resolution target images. Experimental results demonstrate that the proposed method effectively suppresses clutter and improves target detection and tracking capability(The stable tracking range is improved by 60 meters).
- weak target detection,
- deconvolved conventional beamforming,
- signal demodulation,
- clutter suppression,
- non-stationary signal

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References(27)

References

[1]	何福军, 韦小军, 吕浩岩. 浅谈港口水域水下作业通航安全保障方案的重要性及要点[J]. 中国水运, 2025(6): 21-22. He J F, Wei X J, Lv H Y. A brief discussion on the importance and key points of the navigation safety guarantee plan for underwater operations in port waters[J]. China Water Transport, 2025(6): 21-22.
[2]	许钢灿, 倪东波, 郭建. 反蛙人声呐系统发展综述[J]. 中国安全防范技术与应用, 2018(5): 6. Xu G C, Ni D B, Guo J. Overview of the development of anti-frogman sonar system[J]. China Security Protection Technology and Application, 2018(5): 6.
[3]	刘晓东等. 我国海洋声学探测技术竞争力分析[J]. 海洋技术学报, 2015, 34(3): 80-85. Liu X D, et al. Competitiveness Analysis for China's Ocean Acoustic Detection Technologies[J]. Journal of Marine Technology, 2015, 34(3): 80-85.
[4]	刘贯领, 凌国民, 严琪. 主动声呐检测技术的回顾与展望[J]. 声学技术, 2007(2): 335-340. Liu G L, Ling G M, Yan Q. Review and prospect of active sonar detection techniques[J]. Acoustic Technology, 2007(2): 335-340.
[5]	何心怡, 黄海宁, 叶青华. 波束域MVDR高分辨方位估计方法研究[J]. 武汉理工大学学报, 2003(2): 201-204. He X Y, Huang H N, Ye Q H. Research on beam-space MVDR high-resolution direction-of-arrival estimation method[J]. Journal of Wuhan University of Technology, 2003(2): 201-204.
[6]	李关防, 惠俊英. 基于经验模态分解的模态域MVDR方法研究[J]. 电子学报, 2009, 37(5): 942-946. doi: 10.3321/j.issn:0372-2112.2009.05.006 Li G F, Hui J Y. A Study of Mode Domain MVDR Algorithm Based on Empirical Mode Decomposition[J]. Electronic Journal, 2009, 37(5): 942-946. doi: 10.3321/j.issn:0372-2112.2009.05.006
[7]	Mu P C, Li D, Yin Q Y, et al. Robust MVDR beamforming based on covariance matrix reconstruction[J]. Science China (Information Sciences), 2013, 56(4): 28-29. doi: 10.1117/12.913398
[8]	Gruber F K, Marengo E A, Devaney A J. Time-reversal imaging with multiple signal classification considering multiple scattering between the targets[J]. The Journal of the Acoustical Society of America, 2004, 115(6): 3042-3047. doi: 10.1121/1.1738451
[9]	Todros K, Hero A O. Roust multiple signal classification via probability measure transformation[J]. IEEE Transaction on Signal Processing, 2015, 63(5): 1156-1170. doi: 10.1109/TSP.2014.2388436
[10]	Stoica P, Wang Z, Li J. Robust capon beamforming[C]. Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002, 1: 876-880.
[11]	Weber R J, Huang Y. Analysis for Capon and MUSIC DOA estimation algorithms[C]//2009 IEEE Antennas and Propagation Society International Symposium, 2009: 1-4.
[12]	Zuo W, Xin J, Liu C, et al. Improved Capon estimator for high-resolution DOA estimation and its statistical analysis[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10(8): 1716-1729. doi: 10.1109/JAS.2023.123549
[13]	Yang T C. Deconvolved conventional beamforming for a horizontal line[J]. IEEE Journal of Oceanic Engineering, 2018, 43(1): 160-172. doi: 10.1109/JOE.2017.2680818
[14]	孙大军, 康震, 师俊杰, 等. 利用匹配傅里叶变换和二维解卷积的时变线谱信号长时处理方法[J]. 声学学报, 2026: 1-23. Sun D J, Kang Z, Shi J J, et al. Long-term processing method for time-varying line spectrum signals using matched Fourier transform and two-dimensional deconvolution[J]. Acta Acustica, 2026: 1-23.
[15]	吴胜权, 孙晓. 基于改进的反卷积波束形成声源定位方法[J/OL]. 湖南工业大学学报, 2026, (4): 1-9. https://doi.org/10.20271/j.cnki.1673-9833.2026-4003.
[16]	温亚斌. 基于稀疏贝叶斯学习的主动声呐信号处理技术研究[D]. 杭州: 杭州电子科技大学, 2024: 103.
[17]	黄健丽, 王域, 宫在晓, 等. 基于压缩感知的波束域反卷积波束形成算法[J]. 声学学报, 2025, 50(1): 97-108. doi: 10.12395/0371-0025.2023135 Huang J L, Wang Y, Gong Z X, et al. Beam-domain deconvolution beamforming algorithm enhanced by compressive sensing[J]. Acta Acustica, 2025, 50(1): 97-108. doi: 10.12395/0371-0025.2023135
[18]	罗勇强, 王烁杨, 郑逸凡, 等. 基于深度学习的实测短波信号自适应波束形成方法[J]. 现代雷达, 2026: 1-11. Luo Y Q, Wang S Y, Zheng Y F. A Deep Learning-Based Adaptive Beamforming Method for Measured Shortwave Signals [J]. Modern Radar, 2026: 1-11.
[19]	孙振兴, 李雪峰, 南春萍, 等. 基于深度学习的IRS辅助CIoT波束形成[J/OL]. 计算机技术与发展, 1-8. https://doi.org/10.20165/j.cnki.ISSN1673-629X.2025-0356.
[20]	叶钒, 何峰, 朱炬波, 等. 基于目标特性增强的频带缺失雷达信号融合处理[J]. 系统工程与电子技术, 2011, 33(10): 2226-2229. Ye F, He F, Zhu J B, et al. Fusion processing of frequency band missing radar signals based on target characteristic enhancement[J]. Systems Engineering and Electronic Technology, 2011, 33(10): 2226-2229.
[21]	魏尚飞, 韩东, 杨美娇, 等. 基于模态滤波的水下声源深度估计方法[J]. 电声技术, 2021, 45(8): 9-17. Wei S F, Han D, Yang M J, et al. Underwater sound source depth estimation method based on modal filtering[J]. Electroacoustic Technology, 2021, 45(8): 9-17.
[22]	漆保凌. 多模态滤波与形心算法脉冲激光雷达高精度距离-强度信号处理[D]. 哈尔滨: 哈尔滨工业大学, 2024: 132.
[23]	栾经德, 汲长利, 陈锦光, 等. 一种最佳空间滤波器在圆柱型基阵上的实现[J]. 声学技术, 2005(2): 98-100. Luan J D, Ji C L, Chen J G, et al. Implementation of optimum spatial filter on a cylindrical array[J]. Acoustic Technology, 2005(2): 98-100.
[24]	解元, 邹涛, 余锦视, 等. 面向噪声和声学混响场景下的语音增强[J]. 信号处理, 2024, 40(12): 2238-2248. doi: 10.12466/xhcl.2024.12.012 Xie Y, Zhou T, Yu J S, et al. Speech enhancement for noise and acoustic reverberation scenarios[J]. Signal Processing, 2024, 40(12): 2238-2248. doi: 10.12466/xhcl.2024.12.012
[25]	安良, 陈励军, 陆佶人. 海洋噪声背景下基于随机共振方法的波束形成[J]. 声学技术, 2006(2): 98-102. An L, Chen L J, Lu J R. Beamforming based on stochastic resonance with sea noise background[J]. Acoustic Technology, 2006(2): 98-102.
[26]	宗平. 基于随机共振理论的弱信号检测方法研究[D]. 大连: 大连理工大学, 2024: 136.
[27]	吴一全, 童康. 面向海洋的图像处理与目标检测技术与应用综述[J]. 海洋信息技术与应用, 2025, 40(4): 193-205. doi: 10.3969/j.issn.2097-0307.2025.04.001 Wu Y Q, Tong K. Overview of Technologies and Applications of Image Processing and Object Detection for Marine Applications[J]. Journal Of Marine Information Technology And Application, 2025, 40(4): 193-205. doi: 10.3969/j.issn.2097-0307.2025.04.001