加权有向关联网络构建与表征的水中目标远距离检测

doi:10.12382/bgxb.2023.0394

摘要/Abstract

摘要：

水中目标的远距离检测是海洋防御体系的关键技术之一,对国防及民用领域均具有十分重要的作用。然而,目前尚缺乏行之有效的水中目标远距离检测方法,特别是目标先验信息未知的情况下变的愈加困难。为解决这一问题,提出一种新的方法—加权有向关联网络。通过矢量声信号到加权有向关联网络的映射,将信号检测问题转化为网络拓扑的表征,并通过对网络拓扑的特性分析及特征提取,实现无目标先验信息下的水中目标远距离检测。并通过仿真与实测数据对所提出的方法进行验证。研究结果表明:与现有的窄带互谱检测、冒泡熵等方法相比,所提方法能够检测到更低信噪比的水中目标,实现了无需目标先验信息的水中目标远距离检测;该方法的应用具有一定的实际意义和应用前景,可以为海洋防御和民用领域的水下目标检测提供有效的技术支撑。

关键词: 水中目标, 远距离检测, 复杂网络, 矢量声信号, 加权有向关联网络, 无目标先验

Abstract:

Remote detection of underwater targets is a critical technology in ocean defense systems and has significant applications in both national defense and civilian domains. However, there is currently a lack of effective methods for long-range detection of underwater targets, especially without prior knowledge of the target. To address this issue, a new detection method based on weighted directed association network, is proposed, which represents the target signal detection problem as network topology by mapping the vector acoustic signal into the weighted directed association network. The long-range detection of underwater targets without prior knowledge is achieved through the analysis of network topology characteristics and the feature extraction. The proposed method is validated through the simulated and measured data. Compared with existing narrowband mutual spectrum detection and bubble entropy methods, the proposed method can detect the underwater targets with lower signal-to-noise ratios and achieve the long-range detection without prior knowledge. The proposed method has important practical significance and application prospects and can provide the effective technical support for underwater target detection in ocean defense and civilian fields.

Key words: underwater target, long-range detection, complex network, weighted directed correlation network, non-target prior, vector acoustic signal

中图分类号:

TN929.5

张红伟, 王海燕, 闫永胜, 申晓红. 加权有向关联网络构建与表征的水中目标远距离检测[J]. 兵工学报, 2024, 45(8): 2584-2593.

ZHANG Hongwei, WANG Haiyan, YAN Yongsheng, SHEN Xiaohong. Construction and Characterization of Weighted Directed Association Network for Remote Detection of Underwater Targets[J]. Acta Armamentarii, 2024, 45(8): 2584-2593.

图/表 16

图1 不同通道白噪声相关系数值CDF

Fig.1 CDF curve of noise correlation coefficient in different channels

表1 不同通道间变量S与CDF关系

Table 1 Variable S and CDF between different channels

通道	S	F(S)
X-X	0.0231	0.99
X-Y	0.0260	0.99
X-P	0.0250	0.99
Y-Y	0.0222	0.99
Y-P	0.0283	0.99
P-P	0.0229	0.99

图2 高斯白噪声网络拓扑

Fig.2 White Gaussian Noise network topology

图3 Chen系统混沌信号(0dB)网络拓扑

Fig.3 Network topology of Chen system chaotic signal (0dB)

图4 Lorenz系统混沌信号(0dB)网络拓扑

Fig.4 Network topology of Lorenz system chaotic signal (0dB)

图5 Rossler系统混沌信号(0dB)网络拓扑

Fig.5 Network topology of Rossler system chaotic signal (0dB)

图6 Chen系统混沌信号(-10dB)网络拓扑

Fig.6 Network topology for Chen system chaotic signal (-10dB)

图7 Chen系统混沌信号(-15dB)网络拓扑

Fig.7 Network topology for Chen system chaotic signal (-15dB)

图8 Chen系统混沌信号(-20dB)网络拓扑

Fig.8 Network topology for Chen system chaotic signal(-20dB)

图9 Chen系统混沌信号

Fig.9 Chen system chaos signal

图10 3种信号(0dB)以及噪声的WLE

Fig.10 Weighted link entropy of three signals (0dB ) and noises

图11 检测性能对比分析

Fig.11 Comparison of detection performances

图12 试验船走航路线

Fig.12 Sailing path of test ship

图13 频谱图

Fig.13 Sectrogram

图14 时频图

Fig.14 Time-frequency diagram

图15 试验数据检测结果

Fig.15 Data detection results

参考文献 25

[1]	LEROY E C, SAMARAN F, STAFFORD K M, et al. Broad-scale study of the seasonal and geographic occurrence of blue and fin whales in the Southern Indian Ocean[J]. Endangered Species Research, 2018, 37: 289-300.
[2]	马石磊, 王海燕, 申晓红, 等. 复杂海洋环境噪声下甚低频声信号检测方法[J]. 兵工学报, 2020, 41(12):2495-2503.
	MA S L, WANG H Y, SHEN X H, et al. Detection method of VLF acoustic signal in complex marine environment noise[J]. Acta Armamentarii, 2020, 41(12): 2495-2503. (in Chinese)
[3]	YANG H, LI L L, LI G H, et al. A novel feature extraction method for ship-radiated noise[J]. Defence Technology, 2022, 18(4): 604-617. doi: 10.1016/j.dt.2021.03.012
[4]	WEI X X, WANG X W, GAO J, et al. Faulty feeder detection for single-phase-to-ground fault in distribution networks based on transient energy and cosine similarity[J]. IEEE Transactions on Power Delivery, 2022, 37(5): 3968-3979.
[5]	ZHANG D C, XIE M, HAMADACHE M, et al. An adaptive graph morlet wavelet transform for railway wayside acoustic detection[J]. Journal of Sound and Vibration, 2022, 529: 116965.
[6]	XIAO R, JOSEPH P F, MUGGLETON J M, et al. Limits for leak noise detection in gas pipes using cross correlation[J]. Journal of Sound and Vibration, 2022, 520: 116639.
[7]	DENG J, DONG W, SOCHER R, et al. Imagenet: a large-scale hierarchical image database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, US:IEEE, 2009: 248-255.
[8]	WU D, WANG L, ZHANG P. Solving statistical mechanics using variational autoregressive networks[J]. Physical Review Letters, 2019, 122(8): 080602.
[9]	DONG H T, SHEN X H, HE K, et al. Nonlinear filtering effects of intrawell matched stochastic resonance with barrier constrainted duffing system for ship radiated line signature extraction[J]. Chaos, Solitons & Fractals, 2020, 141: 110428.
[10]	SUN Y L, ZHANG X M. Analysis of chaotic characteristics of ship radiated noise signals with different data lengths[C]//Proceedings of OCEANS 2022. Chennai, Indian: IEEE, 2022: 1-7.
[11]	CANBAZ B. Chaos classification in forced fermionic instanton solutions by the Generalized Alignment Index (GALI) and the largest Lyapunov exponent[J]. Chaos, Solitons & Fractals, 2022, 164: 112685.
[12]	BOUNOUA W, BAKDI A. Fault detection and diagnosis of nonlinear dynamical processes through correlation dimension and fractal analysis based dynamic kernel PCA[J]. Chemical Engineering Science, 2021, 229: 116099.
[13]	GAO J X, BARZEL B, BARABÁSIA L. Universal resilience patterns in complex networks[J]. Nature, 2016, 530(7590): 307-312.
[14]	ZHANG H W, WANG H Y, YAN Y S, et al. Weighted dynamic transfer network and spectral entropy for weak nonlinear time series detection[J]. Nonlinear Dynamics, 2023, 111(10):9345-9359.
[15]	LI A, CORNELIUS S P, LIU Y Y, et al. The fundamental advantages of temporal networks[J]. Science, 2017, 358(6366): 1042-1046. doi: 10.1126/science.aai7488 pmid: 29170233
[16]	ZHANG J, SMALL M. Complex network from pseudoperiodic time series: Topology versus dynamics[J]. Physical Review Letters, 2006, 96(23): 238701.
[17]	ZHANG H W, WANG H Y, YAN Y S, et al. Correlation network from multivariate time series: a new method for characterizing nonlinear dynamic behavior in marine acoustic signal[J]. Nonlinear Dynamics, 2023, 111: 13201-13214.
[18]	SONG X J, XIAO F Y. Combining time-series evidence: a complex network model based on a visibility graph and belief entropy[J]. Applied Intelligence, 2022, 52(9): 10706-10715.
[19]	邹勇, DONNER Reik V, MARWAN Norbert, 等. 非线性时间序列的复杂网络分析[J]. 中国科学:物理学力学天文学, 2020, 50(1):133-147.
	ZOU Y, DONNER R V, MARWAN N, et al. Complex network analysis of nonlinear time series[J]. SCIENTIA SINICA Physica, Mechanica & Astronomica, 2020, 50(1): 133-147. (in Chinese)
[20]	ZHU S, ZHANG G J, SHANG Z Z, et al. Design and realization of cap-shaped cilia MEMS vector hydrophone[J]. Measurement, 2021, 183:109818.
[21]	JI T Y, WANG J, LI M S, et al. Short-term wind power forecast based on chaotic analysis and multivariate phase space reconstruction[J]. Energy Conversion and Management, 2022, 254: 115196..
[22]	AKBARI H, SADIQ M T, REHMAN A U, et al. Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features[J]. Applied Acoustics, 2021, 179: 108078.
[23]	ZHANG H W, WANG H Y, YAN Y S, et al. Remote passive sonar detection by relative multiscale change entropy[J]. IEEE Sensors Journal, 2022, 22(18): 18066-18075.
[24]	TOOTOONI M S, RAO P K, CHOU C A, et al. A spectral graph theoretic approach for monitoring multivariate time series data from complex dynamical processes[J]. IEEE Transactions on Automation Science and Engineering, 2016, 15(1): 127-144.
[25]	MANIS G, AKTARUZZAMAN M D, SASSI R. Bubble entropy: an entropy almost free of parameters[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(11): 2711-2718.