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基于线谱特征保持的单枚浮标多目标信号分离算法

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针对单枚被动全向声纳浮标多 目标信号的分离问题，提出联合非负矩阵分解(non-negative matrix factorization，NMF)和快速独立成分分析(fast independent component analysis，FastICA)的多 目标信号盲分离算法。首先，基于空间与谱间相关性优化NMF算法，以增强NMF算法对水声信号调制线谱特征的适应性，提高对线谱的保持优势;然后，以NMF基矩阵优势结合FastICA算法实现水声多 目标信号的盲分离。仿真信号实验结果表明，所提算法取得了较高的信号分离精度，可以较好地保持信号的调制特征，同时对分离信号进行了 一定的降噪增强，更好地保证了后续目标识别的特征支撑。
Multi-target signal separation algorithm for single buoy based on line spectrum feature preservation
To solve the problem of multi-target signal separation of a single passive omnidirectional sonar buoy,a multi-target blind signal separation algorithm combining non-negative matrix factorization(NMF)and fast independent component analysis(FastICA)is proposed.Firstly,the proposed algorithm optimizes the NMF algorithm based on the correlation between space and spectrum to enhance the adaptability of the NMF algorithm to the characteristics of underwater acoustic signal modulation line spectrum and improve the advantage of maintaining line spectrum.Then,the blind separation of underwater acoustic multi-target signals is realized by the FastICA algorithm with NMF basis matrix advantage.Experimental results of simulated signals show that the proposed algorithm achieves high signal separation accuracy,can better maintain the modulation characteristics of the signal,and enhance the noise of the separated signal to a certain extent at the same time,which better ensures the feature support of subsequent target recognition.

passive omnidirectional sonar buoyblind separation of multi-target signalsnon-negative matrix factorization(NMF)fast independent component analysis(FastICA)line spectrum feature preservation

李大卫、吴明辉、单志超、宋广明、蔡召鹏

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海军航空大学航空作战勤务学院,山东烟台 264001

中国人民解放军92635部队,山东青岛 266000

被动全向声纳浮标 多 目标信号盲分离 非负矩阵分解 快速独立成分分析 线谱特征保持

海军航空大学文职基金

I32102003

2024

10.12305/j.issn.1001-506X.2024.06.07

系统工程与电子技术
中国航天科工防御技术研究院 中国宇航学会 中国系统工程学会

系统工程与电子技术

CSTPCD北大核心
影响因子：0.847
ISSN：1001-506X
年,卷(期)：2024.46(6)