Construction and Characterization of Weighted Directed Association Network for Remote Detection of Underwater Targets

doi:10.12382/bgxb.2023.0394

Abstract

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

CLC Number:

TN929.5

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.

Figures/Tables 16

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

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

Fig.2 White Gaussian Noise network topology

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

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

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

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

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

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

Fig.9 Chen system chaos signal

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

Fig.11 Comparison of detection performances

Fig.12 Sailing path of test ship

Fig.13 Sectrogram

Fig.14 Time-frequency diagram

Fig.15 Data detection results

References 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.