A novel feature extraction method for ship-radiated noise

To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.

A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise

Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed RCMDE-KNN.The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and MDE-KNN.It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

Feature extraction of ship-radiated noise using higher-order spectrum

The features of the ship noises are analyzed by using the higher-order spectrum (HOS) after studying their distribution. The results show that the different ship noise has different ranges of the main frequency. The main frequencies of the first class ships are less than 120 Hz, while the second class ships drop in 130 Hz -- 320 Hz. The different relationship between w1 and w2 corresponds to different bispectrum graph. There are the same results in the trispectrum. The feature vector is consist of the wls which correspond to the maximum bispectrum B(wl, wl) and the maximum trispectrum B(wl, w1,wl) respectively, the al, w2 which correspond to the maximum bispectrum B(wl, w2).

一种基于样本熵与EEMD的舰船辐射噪声特征提取方法

为了实现舰船辐射噪声在复杂海洋环境中的特征提取,采用样本熵对3类舰船辐射噪声(SRN)进行特征提取。针对样本熵只能在单尺度下对原信号进行分析,无法有效区分不同类别舰船,提出了一种将样本熵与集合经验模态分解(EEMD)相结合的舰船辐射噪声特征提取方法。首先对3类不同种SRN信号进行EEMD,对分解后得到的各阶固有模态函数(IMF)的样本熵进行分析,选取更具有区分度的最强IMF样本熵作为特征参数。通过比较一定数量3类SRN的最强IMF样本熵及原SRN样本熵特征参数发现,同类舰船的特征参数基本处于同一水平,不同类型的舰船存在一定差异。试验结果表明,以SRN的最强IMF样本熵作为特征参数相比原SRN样本熵对舰船具有更好的可分性。

基于STFT的舰船辐射噪声时-频分析与线谱提取

在分析舰船辐射噪声时频分布的基础上,改进了短时傅里叶变换(ShortTimeFourierTransform,STFT),使之更易于提取舰船辐射噪声中的特征线谱。首先在时域内对将要分析的数据进行低速率重采样,然后根据需要,选择不同宽度的高斯时窗函数作短时傅里叶变换,再用二重三次立方插值函数对分析结果进行平滑处理,得到了清晰的特征线谱。最后利用改进的时频方法对A型舰进行分析,结果表明:该方法对0～100Hz低频段特征线谱的提取效果很好,这为进一步分析其它舰型的低频段特征线谱提供了一种有效方法。

一种估计船舶线谱的联合分析法

线谱是船舶辐射噪声中重要的特征信息 ,线谱提取是水中目标识别的关键技术。文章简述了船舶辐射噪声的物理成因和频谱特征 ,利用经典谱估计法与现代谱估计法在短时序列谱估计中的特点 ,结合Matlab仿真试验的结果 ,提出了一种对船舶辐射噪声 0 10 0Hz低频段线谱估计的联合分析法。并用该方法对两艘实船噪声进行了谱分析 ,提取了A型船在中速航行时 5 .2 5Hz的特征线谱 ,B型船存在 6 5 .5Hz和 6 9.2Hz两条特征线谱。

城市环境噪声评价方法新探

通过对目前城市噪声评价存在的不足之处提出了城市评价需分两步进行，首先采用ＰＮ法进行区域评价，然后用全城区的平均等效声级ＰＳＲＮ法对全城环境噪声进行评价，既体现了区域噪声与城市总体噪声关系，同时考虑了较大起伏的噪声的＂贡献＂．关键应注意合理布设测试点．

诱发脑电信号中工频噪声及其谐波成分的去除

提出了一种去除工频噪声及谐波成分的方法，克服了噪声频率漂移时窄带滤波器所存在的困难，避免了滤波过程引起的附加失真。