基于VMD与共振稀疏分解的舰船辐射噪声窄带特征提取

Narrow-band feature extraction of ship radiated noise based on VMD and resonance sparse decomposition

为了获取实测舰船辐射噪声信号中有效的目标信息、提高低信噪比条件下目标信号的可分性,文章提出了结合变分模态分解(Variational Mode Decomposition,VMD)和共振稀疏分解(Resonance-based Sparsity Signal Decomposition,RSSD)的舰船辐射噪声信号特征提取方法。基于舰船辐射噪声信号具有一定的周期性而外界干扰具有随机性的特点,首先利用VMD自相关分析的方法重构信号,主要剔除带外噪声分量;然后采用RSSD算法基于信号共振属性的不同,进一步滤除带内噪声和瞬态干扰,实现对信号中周期性振荡成分的提取;最后提取信号的波形结构特征用于目标的分类识别。仿真信号与实测信号分析表明,该方法可以较好地滤除带内外噪声,增强舰船辐射噪声信号固有的窄带特征。多类舰船目标的分类实验结果表明,该方法可以有效提高低信噪比信号的可分性,有利于提高目标识别的性能。

In order to obtain effective target information in the measured ship radiated noise signal and improve the separability of target signals under low signal to noise ratio(SNR) condition,a feature extraction method for ship radiated noise signal based on variational mode decomposition(VMD) and resonance-based sparsity signal decomposition(RSSD) is proposed in this paper.Firstly,based on the fact that the ship radiated noise signal is periodic and the noise is random,the VMD autocorrelation analysis method is used to reconstruct the signal and mainly eliminate the out-of-band noise components.Then,based on the different resonance properties of the signal,RSSD algorithm is used to further filter the in-band noise and transient interference,and realize the extraction of periodic oscillation components in the signal.Finally,the waveform structure features of the signal are extracted and used for target classification and recognition.The analysis results of simulation signal and the measured signal analysis show that the method can filter out the out-of-band and in-band noise well and enhance the inherent narrow-band characteristics of ship radiated noise signal.The experimental results of multi-class ship target classification show that this method can effectively improve the separability of low SNR signals and improve the performance of target recognition.