An Underwater Acoustic Target Recognition Algorithm Based on Brain Network Features

doi:10.12382/bgxb.2024.0735

Abstract

Abstract:

In response to the problems of sonar operator having a heavy mental workload and the inability to ensure long-term effective working status in the process of underwater target recognition, a brain network feature-based underwater target recognition algorithm based on brain-computer interface (BCI) technology is proposed to assist sonar operators in achieving the rapid recognition of underwater targets. In order to enhance the extraction of brain neural activity information by the model and reduce the interference of brain irrelevant dependencies, the Granger causality (GC) and transfer entropy (TE) theories are used to reconstruct a brain network feature extraction algorithm, and a underwater acoustic target classification model is established by the proposed algorithm. A visual-auditory joint stimulation paradigm is designed for environmental simulation, and the experimental data is collected to complete the training and validation of the underwater acoustic target classification model. The analyzed results show that the proposed brain network feature algorithm can better capture the dependency information in neural activity. The validation of the underwater acoustic target classification model based on brain network features is verified by the visual-auditory joint stimulation paradigm, and the final recognition accuracy is over 90%.

Key words: brain-computer interface, underwater acoustic target recognition, electroencephalogram, brain network, support vector machine

CLC Number:

R338

ZHANG Jiaqi, SHI Zhangsong, XU Huihui. An Underwater Acoustic Target Recognition Algorithm Based on Brain Network Features[J]. Acta Armamentarii, 2025, 46(5): 240735-.

Figures/Tables 12

References 35

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试次对	龙虾		海豚		蝠鲼
试次对	加权 kappa值	显著性	加权 kappa值	显著性	加权 kappa值	显著性
1-2	0.828	<0.001	0.832	<0.001	0.825	<0.001
1-3	0.809	<0.001	0.825	<0.001	0.829	<0.001
1-4	0.839	<0.001	0.833	<0.001	0.828	<0.001
1-5	0.795	<0.001	0.837	<0.001	0.866	<0.001
1-6	0.829	<0.001	0.813	<0.001	0.821	<0.001
2-3	0.841	<0.001	0.829	<0.001	0.830	<0.001
2-4	0.854	<0.001	0.870	<0.001	0.855	<0.001
2-5	0.843	<0.001	0.855	<0.001	0.845	<0.001
2-6	0.845	<0.001	0.838	<0.001	0.811	<0.001
3-4	0.840	<0.001	0.833	<0.001	0.828	<0.001
3-5	0.819	<0.001	0.852	<0.001	0.877	<0.001
3-6	0.831	<0.001	0.825	<0.001	0.849	<0.001
4-5	0.864	<0.001	0.857	<0.001	0.853	<0.001
4-6	0.813	<0.001	0.828	<0.001	0.831	<0.001
5-6	0.806	<0.001	0.807	<0.001	0.851	<0.001

试次对	龙虾		海豚		蝠鲼
试次对	加权 kappa值	显著性	加权 kappa值	显著性	加权 kappa值	显著性
1-2	0.828	<0.001	0.832	<0.001	0.825	<0.001
1-3	0.809	<0.001	0.825	<0.001	0.829	<0.001
1-4	0.839	<0.001	0.833	<0.001	0.828	<0.001
1-5	0.795	<0.001	0.837	<0.001	0.866	<0.001
1-6	0.829	<0.001	0.813	<0.001	0.821	<0.001
2-3	0.841	<0.001	0.829	<0.001	0.830	<0.001
2-4	0.854	<0.001	0.870	<0.001	0.855	<0.001
2-5	0.843	<0.001	0.855	<0.001	0.845	<0.001
2-6	0.845	<0.001	0.838	<0.001	0.811	<0.001
3-4	0.840	<0.001	0.833	<0.001	0.828	<0.001
3-5	0.819	<0.001	0.852	<0.001	0.877	<0.001
3-6	0.831	<0.001	0.825	<0.001	0.849	<0.001
4-5	0.864	<0.001	0.857	<0.001	0.853	<0.001
4-6	0.813	<0.001	0.828	<0.001	0.831	<0.001
5-6	0.806	<0.001	0.807	<0.001	0.851	<0.001

算法	特征集维度	测试准确率/%	标准差
GT	185	87.58	2.73
GC	302	68.84	8.59
TE	203	56.23	9.26

算法	特征集维度	测试准确率/%	标准差
GT	185	87.58	2.73
GC	302	68.84	8.59
TE	203	56.23	9.26

算法	测试准确率/%	标准差
脑网络+LSTM	94.16	0.0284
MFCC+LSTM	84.93	0.0308