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 s...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.展开更多
Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction ...Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.展开更多
研究了船舶螺旋桨噪声和轴系噪声的噪声包络调制检测(detection of envelope modulation on noise, DEMON)谱的轴频线谱相位特性,提出了一种新的船舶目标特征即船舶螺旋桨噪声与轴系噪声轴频线谱之间的相位差。通过对船舶辐射噪声中螺...研究了船舶螺旋桨噪声和轴系噪声的噪声包络调制检测(detection of envelope modulation on noise, DEMON)谱的轴频线谱相位特性,提出了一种新的船舶目标特征即船舶螺旋桨噪声与轴系噪声轴频线谱之间的相位差。通过对船舶辐射噪声中螺旋桨噪声和轴系噪声轴频线谱相位的分析,指出二者相位存在一个较稳定的相位差。通过建模和仿真验证了相位差提取的可行性,并据此提出一种船舶螺旋桨噪声与轴系噪声的轴频线谱相位差特征提取方法。利用文章提出的特征提取方法,对实验中获取的船舶辐射噪声数据进行相位差特征提取,研究了在不同航速和相同型号不同个体目标船舶两种条件下,相位差特征的稳定性和可分性。结果表明在一定的时间窗内,相位差特征在不同航速下具有较好的稳定性,在相同型号船舶间具有较好的可分性,可以为个体船舶目标识别提供参考。展开更多
An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode ...An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.展开更多
基金supported by the National Natural Science Foundation of China under Grant 51709228。
文摘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.
基金supported by the National Natural Science Foundation of China(62141108)Natural Science Foundation of Tianjin(19JCQNJC01000)。
文摘Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.
文摘研究了船舶螺旋桨噪声和轴系噪声的噪声包络调制检测(detection of envelope modulation on noise, DEMON)谱的轴频线谱相位特性,提出了一种新的船舶目标特征即船舶螺旋桨噪声与轴系噪声轴频线谱之间的相位差。通过对船舶辐射噪声中螺旋桨噪声和轴系噪声轴频线谱相位的分析,指出二者相位存在一个较稳定的相位差。通过建模和仿真验证了相位差提取的可行性,并据此提出一种船舶螺旋桨噪声与轴系噪声的轴频线谱相位差特征提取方法。利用文章提出的特征提取方法,对实验中获取的船舶辐射噪声数据进行相位差特征提取,研究了在不同航速和相同型号不同个体目标船舶两种条件下,相位差特征的稳定性和可分性。结果表明在一定的时间窗内,相位差特征在不同航速下具有较好的稳定性,在相同型号船舶间具有较好的可分性,可以为个体船舶目标识别提供参考。
基金Project(61573381)supported by the National Natural Science Foundation of ChinaProject(2012AA051601)supported by the National High-tech Research and Development Program of China
文摘An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.
