Correlation Method of Acoustic Characteristics of Multiple Targets during Water Entry

doi:10.12382/bgxb.2023.0250

Abstract

Abstract:

The underwater acoustic signal generated by a target entering the water is complex which can easily cause false alarm and miss due to the impulsive interference in ocean. In addition, it is difficult to correctly correlate the underwater acoustic signals of the same target between different observation nodes when multiple targets enter the water at different intervals. In order to solve the above problems, a multi-target water-entry acoustic signals correlation method is proposed. The detected transient signal is decomposed into the intrinsic mode function (IMF) through empirical mode decomposition, and the characteristics of the frequency spectrum and so on are extracted. The grey relational analysis method and fuzzy C-means clustering algorithm are used to realize the correlation of multi-target water-entry acoustic signals, providing necessary conditions for the subsequent distributed positioning. The simulated results show that the target correlation accuracy of the two methods can reach 98% and 87%, respectively,when the number of target feature parameters is 6, and the target correlation accuracy of the two methods can reach 95% and 87%, respectively, when the number of observation nodes is 7. The simulated results prove the feasibility and effectiveness of the proposed method.

Key words: water-entry acoustic signal, multi-target correlation, transient signal, feature correlation

CLC Number:

TB566

LI Yanhe, ZOU Nan, ZHANG Limin, LIU Bing, XIU Xian, WU Zongzheng, FU Keyi. Correlation Method of Acoustic Characteristics of Multiple Targets during Water Entry[J]. Acta Armamentarii, 2024, 45(6): 1921-1932.

Figures/Tables 17

Fig.1 Problem model

Fig.2 Flow chart of grey relational analysis

Fig.3 Flow chart of fuzzy C-means clustering algorithm

Fig.4 Coordinate map of observation nodes and water-entry points

Table 1 Time delay table of target signal received by each observation node

时延/s	Q₁	Q₂	Q₃
$τ P 1$	0.53	2.32	2.25
$τ P 2$	0.80	1.73	1.36
$τ P 3$	1.55	0.60	2.01

Table 1 Time delay table of target signal received by each observation node

时延/s	Q₁	Q₂	Q₃
$τ P 1$	0.53	2.32	2.25
$τ P 2$	0.80	1.73	1.36
$τ P 3$	1.55	0.60	2.01

Fig.5 Background noise of observation nodes

Fig.6 Time-domain signal received by each observation node

Fig.7 Underwater acoustic time-domain and frequency-domain signals Q1, Q2 and Q3

Fig.8 EMD decomposition result of underwater acoustic signal Q1 received by P1

Fig.9 EMD decomposition result of underwater acoustic signal Q1 received by P2

Fig.10 EMD decomposition result of underwater acoustic signal Q1 received by P3

Fig.11 EMD decomposition result of underwater acoustic signal Q3 received by P1

Table 2 Grey relational degree

	P₁₁	P₁₂	P₂₁	P₂₂	P₂₃	P₃₁	P₃₂	P₃₃
P₁₁	1.000	0.692	0.902	0.637	0.572	0.570	0.900	0.688
P₁₂	0.692	1.000	0.684	0.865	0.620	0.571	0.539	0.829
P₂₁	0.902	0.684	1.000	0.644	0.569	0.566	0.924	0.688
P₂₂	0.637	0.865	0.644	1.000	0.544	0.541	0.604	0.867
P₂₃	0.572	0.620	0.569	0.544	1.000	0.752	0.561	0.607
P₃₁	0.570	0.571	0.566	0.541	0.752	1.000	0.579	0.564
P₃₂	0.900	0.539	0.924	0.604	0.561	0.579	1.000	0.557
P₃₃	0.688	0.829	0.688	0.867	0.607	0.564	0.557	1.000

Table 3 Clustering status

聚类划分	类内紧凑度	类间分离度	指标函数
聚簇1:P₁₁ 聚簇2:P₁₂ 聚簇3:P₂₂ 聚簇4:P₂₃ 聚簇5:P₃₁ 聚簇6:P₃₃ 聚簇7:P₂₁,P₃₂	0.0247	0.3289	0.3536
聚簇1:P₁₂ 聚簇2:P₂₂ 聚簇3:P₂₃ 聚簇4:P₃₁ 聚簇5:P₃₃ 聚簇6:P₁₁,P₂₁,P₃₂	0.0422	0.3269	0.3691
聚簇1:P₁₂ 聚簇2:P₂₃ 聚簇3:P₃₁ 聚簇4:P₂₂,P₃₃ 聚簇5:P₁₁,P₂₁,P₃₂	0.0852	0.3165	0.4017
聚簇1:P₂₃ 聚簇2:P₃₁ 聚簇3:P₁₁,P₂₁,P₃₂ 聚簇4:P₁₂,P₂₂,P₃₃	0.1265	0.3063	0.4329
聚簇1:P₁₂,P₂₂,P₃₃ 聚簇2:P₁₁,P₂₁,P₃₂ 聚簇3:P₂₃,P₃₁	0.2264	0.2968	0.5232
聚簇1:P₁₁,P₁₂,P₂₁, P₂₂,P₃₂,P₃₃ 聚簇2:P₂₃,P₃₁	0.2216	0.2859	0.5075

Table 4 Subordination degree

聚簇	P₁₁	P₁₂	P₂₁	P₂₂	P₂₃	P₃₁	P₃₂	P₃₃
聚簇1	0.0033	0.0073	0.0019	0.0498	0.9791	0.9800	0.0072	0.0241
聚簇2	0.0031	0.9836	0.0019	0.8809	0.0093	0.0090	0.0079	0.9493
聚簇3	0.9936	0.0091	0.9962	0.0693	0.0116	0.0110	0.9848	0.0266

Fig.12 Number of target feature parameters and correlation accuracy

Fig.13 Number of observation nodes and correlation accuracy

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