Alpha稳定分布噪声下的水声瞬态信号检测方法

陈雯; 邹男; 张光普; 李研赫

doi:10.11999/JEIT250500

Alpha稳定分布噪声下的水声瞬态信号检测方法

doi: 10.11999/JEIT250500 cstr: 32379.14.JEIT250500

陈雯^{1, 2, 3},
邹男^{1, 2, 3, ,},
张光普^{1, 2, 3},
李研赫^{1, 2, 3}

1.
哈尔滨工程大学，水声技术全国重点实验室哈尔滨 150001
2.
海洋信息获取与安全工信部重点实验室(哈尔滨工程大学)，工业和信息化部哈尔滨 150001
3.
哈尔滨工程大学，水声工程学院哈尔滨 150001

基金项目: 水声技术全国重点实验室基金(KY10500230140)，声纳技术重点实验室基金(2023-JCJQ-LB-32/09)

详细信息

作者简介:
陈雯：女，硕士，研究方向为水声信号处理

邹男：女，副教授，博士生教师，研究方向为水下目标定位与导航、水下目标识别、水声信号处理等

张光普：男，教授，博士生教师，研究方向为水下目标定位与导航、声纳系统设计、水声信号处理等

李研赫：女，硕士，研究方向为水声信号处理

通讯作者:
邹男　zounan@hrbeu.edu.cn

中图分类号: TB566
计量
- 文章访问数: 40
- HTML全文浏览量: 29
- PDF下载量: 15
- 被引次数: 0
出版历程
- 修回日期: 2025-11-12
- 录用日期: 2025-11-12
- 网络出版日期: 2025-11-17

Detection of underwater acoustic transient signals under Alpha stable distribution noise

CHEN Wen^{1, 2, 3},
ZOU Nan^{1, 2, 3
, ,},
ZHANG Guangpu^{1, 2, 3},
LI Yanhe^{1, 2, 3}

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University), Ministry of Industry and Information Technology; Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

摘要

摘要: 实际海洋环境噪声通常具有突发性尖峰等非高斯特性，导致基于高斯假设的传统信号检测方法性能显著下降甚至失效。针对非高斯背景下未知确定性瞬态信号的被动检测与到达时间(ToA)估计的问题，该文利用Alpha稳定分布为非高斯背景噪声建模并设计了一种数据预处理降噪-短时互相关熵检测(DP-STCCD)方法。该方法首先借鉴数据清洗思想对含噪信号进行异常值处理实现初级降噪，然后采用多级滤波技术进一步抑制噪声，最终利用降噪信号的短时互相关熵特征构建检测器并实现ToA估计。仿真结果表明，经预处理降噪的能量检测器(DP-ED)在一定程度上恢复了检测性能。但在相同条件下，DP-STCCD算法的检测性能与ToA估计精度显著优于DP-ED算法：当特征指数$ \alpha = 1.5 $时，在–11 dB低广义信噪比下DP-STCCD检测概率较DP-ED仍提高约30.2%，ToA估计精度提高约18.4%。
- Alpha稳定分布 /
- 相关熵 /
- 瞬态信号检测 /
- 非高斯噪声
Abstract: Objective Transient signals are generated by state changes of underwater acoustic targets and are difficult to suppress or eliminate, thus becoming an important means for covert underwater target detection. However, practical marine environmental noise exhibits non-Gaussian characteristics, such as impulsive spikes.That severely degrade or even invalidate traditional Gaussian-based detection methods, particularly energy detection widely used in engineering applications. While existing studies employ nonlinear transformations or fractional lower-order statistics to address non-Gaussian noise, there are limitations such as signal distortion and high computational complexity. To overcome these challenges, the Alpha-stable distribution is adopted to replace traditional Gaussian modeling, and a Data Preprocessing denoising- Short-Time Cross-Correntropy Detection (DP-STCCD) method is proposed to achieve passive detection and Time of Arrival (ToA) estimation for unknown deterministic transient signals in non-Gaussian noise environments. Methods The proposed method comprises two stages: data preprocessing denoising and short-time cross-correntropy detection. In the data preprocessing stage, an outlier detection technique based on the Interquartile Range (IQR) is applied. Upper and lower thresholds are calculated to effectively remove impulsive spikes while preserving local signal features. Then multi-stage filtering is employed to further suppress noise: median filtering reconstructs the signal with minimal detail loss, modified mean filtering eliminates residual spikes by discarding extreme values in local windows. In the detection stage, the denoised signal is segmented into frames. Short-time cross-correntropy based on a Gaussian kernel is computed between adjacent frames to construct detection statistics. A first-order recursive filter estimates background noise to set detection thresholds. Joint amplitude-width decision logic generates detection results. ToA estimation is achieved by identifying peaks in the short-time cross-correntropy. This method eliminates dependence on prior noise knowledge and enhances robustness in non-Gaussian environments through data cleaning and information-theoretic feature extraction. Results and Discussions Simulations under standard symmetric Alpha-stable distributed noise validate the algorithm’s performance. The data preprocessing denoising algorithm effectively eliminates impulsive spikes while retaining critical time-domain signal characteristics (Fig.3). After denoising, the detection performance of the energy detector is partially restored. And the peak-to-average ratio of short-time cross-correntropy features improves by 10 dB (Fig. 4, Fig. 5). Experimental results demonstrate that DP-STCCD significantly outperforms Data Preprocessing denoising-Energy Detection(DP-ED) in both detection probability and ToA estimation accuracy under identical conditions. At the characteristic index $ \mathrm{\alpha } $=1.5 and Generalized SNR (GSNR) of -11 dB, DP-STCCD achieves a 30.2% higher detection probability and an 18.4% improvement in ToA estimation precision compared to DP-ED (Fig. 6, Fig. 9(a)). These results validate the effectiveness and robustness of the proposed method in complex noise environments. Conclusions A joint detection method DP-STCCD integrating data preprocessing denoising and short-time cross-correntropy features is proposed to address underwater transient signal detection under Alpha-stable distributed noise. Preprocessing techniques, including IQR-based outlier detection and multi-stage filtering, effectively suppress impulsive interference while preserving key signal characteristics. The short-time cross-correntropy feature enhances detection sensitivity and ToA estimation accuracy. Results indicate that the proposed method outperforms traditional energy detectors under low GSNR conditions and maintains superior stability across varying characteristic indices. This study provides a novel approach for covert underwater target detection in non-Gaussian noise environments. Future work will focus on optimizing the algorithm for practical marine noise interference to enhance its engineering applicability.
- Non-Gaussian noise /
- Alpha-stable distribution /
- Correntropy /
- Transient signal detection

HTML全文

图 1 降噪算法流程图

下载: 全尺寸图片幻灯片

图 2 短时互相关熵检测算法流程图

下载: 全尺寸图片幻灯片

图 3 信号模型

下载: 全尺寸图片幻灯片

图 4 数据预处理降噪结果

下载: 全尺寸图片幻灯片

图 5 检测结果

下载: 全尺寸图片幻灯片

图 6 ROC曲线($ \alpha = 1.5 $，$ \sigma = 1 $)

下载: 全尺寸图片幻灯片

图 7 特征指数影响分析($ \sigma = 1 $)

下载: 全尺寸图片幻灯片

图 8 核参数影响分析($ \alpha = 1.5 $)

下载: 全尺寸图片幻灯片

图 9 ToA估计误差分析

下载: 全尺寸图片幻灯片

参考文献(25)

[1]	梁国龙, 张博宇, 齐滨, 等. 无源声呐水下多目标融合跟踪方法[J]. 声学学报, 2024, 49(3): 501–512. doi: 10.12395/0371-0025.2022188. LIANG Guolong, ZHANG Boyu, QI Bin, et al. Underwater multitarget fusion tracking method for passive sonar[J]. Acta Acustica, 2024, 49(3): 501–512. doi: 10.12395/0371-0025.2022188.
[2]	杨振, 邹男, 付进. Hilbert-Huang变换在瞬态信号检测中的应用[J]. 声学技术, 2015, 34(2): 167–171. doi: 10.16300/j.cnki.1000-3630.2015.02.013. YANG Zhen, ZOU Nan, and FU Jin. Application of Hilbert-Huang transform in transient signal detection[J]. Technical Acoustics, 2015, 34(2): 167–171. doi: 10.16300/j.cnki.1000-3630.2015.02.013.
[3]	LU Ning and EISENSTEIN B. Detection of weak signals in non-Gaussian noise[J]. IEEE Transactions on Information Theory, 1981, 27(6): 755–771. doi: 10.1109/TIT.1981.1056414.
[4]	LIU Haotian, MA Lu, WANG Zhaohui, et al. Channel prediction for underwater acoustic communication: A review and performance evaluation of algorithms[J]. Remote Sensing, 2024, 16(9): 1546. doi: 10.3390/rs16091546.
[5]	NIKIAS C L and SHAO Min. Signal Processing with Alpha-Stable Distributions and Applications[M]. New York: Wiley, 1995.
[6]	谭靖骞, 曹宇, 黄海宁, 等. 北极海域海洋环境噪声建模与特性分析[J]. 应用声学, 2020, 39(5): 690–697. doi: 10.11684/j.issn.1000-310X.2020.05.006. TAN Jingqian, CAO Yu, HUANG Haining, et al. Modeling and characterization of marine ambient noise in the Arctic[J]. Journal of Applied Acoustics, 2020, 39(5): 690–697. doi: 10.11684/j.issn.1000-310X.2020.05.006.
[7]	宋国丽, 郭新毅, 马力. 海洋环境噪声中的α稳定分布模型[J]. 声学学报, 2019, 44(2): 177–188. doi: 10.15949/j.cnki.0371-0025.2019.02.004. SONG Guoli, GUO Xinyi, and MA Li. α stable distribution model in ocean ambient noise[J]. Acta Acustica, 2019, 44(2): 177–188. doi: 10.15949/j.cnki.0371-0025.2019.02.004.
[8]	冯晓, 周明章, 张学波, 等. 浅海非高斯噪声下基于变分贝叶斯推断的波达角估计[J]. 电子与信息学报, 2022, 44(6): 1887–1896. doi: 10.11999/JEIT211284. FENG Xiao, ZHOU Mingzhang, ZHANG Xuebo, et al. Variational Bayesian inference based direction of arrival estimation in presence of shallow water non-Gaussian noise[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1887–1896. doi: 10.11999/JEIT211284.
[9]	王平波, 代振, 卫红凯. 基于SαS分布的高斯化处理研究[J]. 电子与信息学报, 2020, 42(9): 2239–2245. doi: 10.11999/JEIT190539. WANG Pingbo, DAI Zhen, and WEI Hongkai. Study of Gaussianization processing based on symmetric alpha-stable distribution modeling[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2239–2245. doi: 10.11999/JEIT190539.
[10]	吕玉娇, 黄海宁, 张扬帆, 等. 冰下非高斯噪声下的范数约束波束形成方法[J]. 声学学报, 2024, 49(2): 286–297. doi: 10.12395/0371-0025.2022137. LÜ Yujiao, HUANG Haining, ZHANG Yangfan, et al. Norm-constraining beamforming amid under-ice non-Gaussian noise[J]. Acta Acustica, 2024, 49(2): 286–297. doi: 10.12395/0371-0025.2022137.
[11]	MA Jitong, HU Mutian, WANG Tianyu, et al. Automatic modulation classification in impulsive noise: Hyperbolic-tangent cyclic spectrum and multibranch attention shuffle network[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 5501613. doi: 10.1109/TIM.2023.3244798.
[12]	CHENG Yabing, LI Chao, CHEN Shuming, et al. An enhanced impulse noise control algorithm using a novel nonlinear function[J]. Circuits, Systems, and Signal Processing, 2023, 42(11): 6524–6543. doi: 10.1007/s00034-023-02421-3.
[13]	THANH D N H, PRASATH V B S, PHUNG T K, et al. Impulse denoising based on noise accumulation and harmonic analysis techniques[J]. Optik, 2021, 241: 166163. doi: 10.1016/j.ijleo.2020.166163.
[14]	URKOWITZ H. Energy detection of unknown deterministic signals[J]. Proceedings of the IEEE, 1967, 55(4): 523–531. doi: 10.1109/PROC.1967.5573.
[15]	JIA Li, DAI Wenshu, ZHANG Guojun, et al. Improved frequency detection capability of MEMS bionic vector hydrophone in low signal-to-noise ratio environment[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 7501910. doi: 10.1109/TIM.2025.3544358.
[16]	郑作虎, 王首勇. 基于Alpha稳定分布杂波模型的雷达目标检测方法[J]. 电子与信息学报, 2014, 36(12): 2963–2968. doi: 10.3724/SP.J.1146.2014.00072. ZHENG Zuohu and WANG Shouyong. Radar target detection method based on the alpha-stable distribution clutter model[J]. Journal of Electronics & Information Technology, 2014, 36(12): 2963–2968. doi: 10.3724/SP.J.1146.2014.00072.
[17]	舒彤, 余香梅, 查代奉, 等. Alpha稳定分布噪声条件下的广义S变换时频分析[J]. 信号处理, 2014, 30(6): 634–641. doi: 10.3969/j.issn.1003-0530.2014.06.003. SHU Tong, YU Xiangmei, ZHA Daifeng, et al. Time-frequency distribution of generalized S-transform under Alpha stable distributed noise conditions[J]. Journal of Signal Processing, 2014, 30(6): 634–641. doi: 10.3969/j.issn.1003-0530.2014.06.003.
[18]	MA Jitong, ZHANG Jiacheng, YANG Zhengyan, et al. Super-resolution time delay estimation using exponential kernel correlation in impulsive noise and multipath environments[J]. Digital Signal Processing, 2023, 133: 103882. doi: 10.1016/j.dsp.2022.103882.
[19]	DONG Xudong, SUN Meng, ZHANG Xiaofei, et al. Fractional low-order moments based DOA estimation with co-prime array in presence of impulsive noise[J]. IEEE Access, 2021, 9: 23537–23543. doi: 10.1109/ACCESS.2021.3057381.
[20]	邱天爽. 相关熵与循环相关熵信号处理研究进展[J]. 电子与信息学报, 2020, 42(1): 105–118. doi: 10.11999/JEIT190646. QIU Tianshuang. Development in signal processing based on correntropy and cyclic correntropy[J]. Journal of Electronics & Information Technology, 2020, 42(1): 105–118. doi: 10.11999/JEIT190646.
[21]	LIU Weifeng, POKHAREL P P, and PRINCIPE J C. Correntropy: Properties and applications in non-Gaussian signal processing[J]. IEEE Transactions on Signal Processing, 2007, 55(11): 5286–5298. doi: 10.1109/TSP.2007.896065.
[22]	HE Ran, ZHENG Weishi, and HU Baogang. Maximum correntropy criterion for robust face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1561–1576. doi: 10.1109/TPAMI.2010.220.
[23]	ZHAO Jun, GUI Renzhou, DONG Xudong, et al. Direction of arrival estimation with nested arrays in presence of impulsive noise: A correlation entropy-based infinite norm strategy[J]. Remote Sensing, 2023, 15(22): 5345. doi: 10.3390/rs15225345.
[24]	SHI Long, SHEN Lu, and CHEN Badong. An efficient parameter optimization of maximum correntropy criterion[J]. IEEE Signal Processing Letters, 2023, 30: 538–542. doi: 10.1109/LSP.2023.3273174.
[25]	ZHANG Yingqiao, FANG Zhiying, and FAN Jun. Generalization analysis of deep CNNs under maximum correntropy criterion[J]. Neural Networks, 2024, 174: 106226. doi: 10.1016/j.neunet.2024.106226.