To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. ...To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.展开更多
The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deterior...The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.展开更多
Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects...Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.展开更多
针对射频指纹识别中单一特征无法全面表示信号的完整性,且类间特征差异较小从而限制识别准确率等问题,提出了一种基于时频和双谱特征融合的DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)射频指纹识别方法。首先,对采集到...针对射频指纹识别中单一特征无法全面表示信号的完整性,且类间特征差异较小从而限制识别准确率等问题,提出了一种基于时频和双谱特征融合的DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)射频指纹识别方法。首先,对采集到的不同设备的信号分别进行短时傅里叶变换(short-time Fourier transform,STFT)和双谱变换,将得到的图像二值化处理并拼接,综合利用两种变换分别在时频域和高阶统计特性上的优势,更全面地提取和表征不同设备的射频指纹特征;然后,提出了DA-ResNeXt50网络模型,借鉴密集连接思想,使四层残差单元每一层都与前面所有层直接相连,促进了特征的复用和传递,能更好地捕捉类间细微差异;最后,使用非对称卷积模块(asymmetric convolution block,ACBlock)替换模型最后一层残差单元的3×3卷积,可以有效地增加网络的感受野,增强卷积核的骨架部分,从而提高射频指纹识别性能。实验结果表明,相较于使用单一特征提取方法,提出的特征融合方法的性能有较大的提升,改进后的模型与多种经典模型相比,具有较高的识别精度。展开更多
基金the National Natural Science Foundation of China (90407007 60372001).
文摘To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.
文摘The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.
基金Project(51675262)supported by the National Natural Science Foundation of ChinaProject(2016YFD0700800)supported by the National Key Research and Development Program of China+2 种基金Project(6140210020102)supported by the Advance Research Field Fund Project of ChinaProject(NP2018304)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2017-IV-0008-0045)supported by the National Science and Technology Major Project
文摘Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.
文摘针对射频指纹识别中单一特征无法全面表示信号的完整性,且类间特征差异较小从而限制识别准确率等问题,提出了一种基于时频和双谱特征融合的DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)射频指纹识别方法。首先,对采集到的不同设备的信号分别进行短时傅里叶变换(short-time Fourier transform,STFT)和双谱变换,将得到的图像二值化处理并拼接,综合利用两种变换分别在时频域和高阶统计特性上的优势,更全面地提取和表征不同设备的射频指纹特征;然后,提出了DA-ResNeXt50网络模型,借鉴密集连接思想,使四层残差单元每一层都与前面所有层直接相连,促进了特征的复用和传递,能更好地捕捉类间细微差异;最后,使用非对称卷积模块(asymmetric convolution block,ACBlock)替换模型最后一层残差单元的3×3卷积,可以有效地增加网络的感受野,增强卷积核的骨架部分,从而提高射频指纹识别性能。实验结果表明,相较于使用单一特征提取方法,提出的特征融合方法的性能有较大的提升,改进后的模型与多种经典模型相比,具有较高的识别精度。
