多目标击水声信号特征关联方法

doi:10.12382/bgxb.2023.0250

摘要/Abstract

摘要：

目标入水产生的击水声信号形式复杂,在海洋尖刺干扰的影响下易造成漏报和虚警,当多目标不等间隔陆续入水,不同观测节点间同一目标的击水声信号难以正确关联。针对上述问题,提出一种多目标击水声信号关联方法。通过经验模态分解将检测到的瞬态信号分解为固有模态函数(Intrinsic Mode Function, IMF),并提取各阶IMF的频谱等特征,利用灰色关联分析,模糊C均值聚类两种算法实现多击水声信号的关联,为后续的分布式定位提供必要条件。仿真结果表明:当特征参数个数为6时,两种方法的目标关联正确率分别可达98%和87%;当测量节点个数为7时,两种方法的目标关联正确率分别可达95%和87%。仿真结果证明了本文方法的可行性和有效性。

关键词: 击水声信号, 多目标关联, 瞬态信号, 特征关联

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

中图分类号:

TB566

李研赫, 邹男, 张丽敏, 刘冰, 修贤, 吴宗铮, 傅可一. 多目标击水声信号特征关联方法[J]. 兵工学报, 2024, 45(6): 1921-1932.

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.

图/表 17

图1 问题模型

Fig.1 Problem model

图2 灰色关联分析流程图

Fig.2 Flow chart of grey relational analysis

图3 模糊C均值聚类关联算法流程图

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

图4 观测节点与目标入水位置坐标图

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

表1 各观测节点接收目标信号时延

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

表1 各观测节点接收目标信号时延

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

图5 观测节点背景噪声

Fig.5 Background noise of observation nodes

图6 各观测节点接收到的时域信号

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

图7 Q1、Q2、Q3入水声时域及频域信号

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

图8 P1接收到Q1入水声信号的EMD分解结果

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

图9 P2接收到Q1入水声信号的EMD分解结果

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

图10 P3接收到Q1入水声信号的EMD分解结果

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

图11 P1接收到Q3入水声信号的EMD分解结果

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

表2 灰色关联度

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

表3 聚类划分状态

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

表4 隶属度

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

图12 目标特征参数个数与关联正确率

Fig.12 Number of target feature parameters and correlation accuracy

图13 观测节点个数与关联正确率

Fig.13 Number of observation nodes and correlation accuracy

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