Ground Penetration Radar is a controlled source geophysical method which uses high frequency electromagnetic waves to study shallow layers. Resolution of this method depends on difference of electrical properties betw...Ground Penetration Radar is a controlled source geophysical method which uses high frequency electromagnetic waves to study shallow layers. Resolution of this method depends on difference of electrical properties between target and surrounding electrical medium, target geometry and used bandwidth. The wavelet transform is used extensively in signal analysis and noise attenuation. In addition, wavelet domain allows local precise descriptions of signal behavior. The Fourier coefficient represents a component for all time and therefore local events must be described by the phase characteristic which can be abolished or strengthened over a large period of time. Finally basis of Auto Regression (AR) is the fitting of an appropriate model on data, which in practice results in more information from data process. Estimation of the parameters of the regression model (AR) is very important. In order to obtain a higher-resolution spectral estimation than other models, recursive operator is a suitable tool. Generally, it is much easier to work with an Auto Regression model. Results shows that the TQWT in soft thresholding mode can attenuate random noise far better than TQWT in hard thresholding mode and Autoregressive-FX method.展开更多
Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing ...Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing the difficulty of the feature extraction.Thereby,a novel denoising method based on the tunable Q-factor wavelet transform(TQWT)using neighboring coefficients is proposed in this article.The emerging TQWT possesses excellent properties compared with the conventional constant-Q wavelet transforms,which can tune Q-factor according to the oscillatory behavior of the signal.Meanwhile,neighboring coefficients denoising is adopted to avoid the overkill of conventional term-by-term thresholding techniques.Because of having the combined advantages of the two methods,the presented denoising method is more practical and effective than other methods.The proposed method is applied to a simulated signal,a rolling element bearing with an outer race defect from antenna transmission chain and a gearbox fault detection case.The processing results demonstrate that the proposed method can successfully identify the fault features,showing that this method is more effective than the conventional wavelet thresholding denoising methods,term-by-term TQWT denoising schemes and spectral kurtosis.展开更多
Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wave...Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wavelet transform(TQWT)for moving target detection.Firstly,this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform(FRFT)domain.Then,TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target’s oscillation characteristics,using the basis pursuit denoising(BPDN)algorithm to get the wavelet coefficients.Furthermore,an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target.Finally,experiments on the Council for Scientific and Industrial Research(CSIR)dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.展开更多
This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is m...This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT;similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.展开更多
文摘Ground Penetration Radar is a controlled source geophysical method which uses high frequency electromagnetic waves to study shallow layers. Resolution of this method depends on difference of electrical properties between target and surrounding electrical medium, target geometry and used bandwidth. The wavelet transform is used extensively in signal analysis and noise attenuation. In addition, wavelet domain allows local precise descriptions of signal behavior. The Fourier coefficient represents a component for all time and therefore local events must be described by the phase characteristic which can be abolished or strengthened over a large period of time. Finally basis of Auto Regression (AR) is the fitting of an appropriate model on data, which in practice results in more information from data process. Estimation of the parameters of the regression model (AR) is very important. In order to obtain a higher-resolution spectral estimation than other models, recursive operator is a suitable tool. Generally, it is much easier to work with an Auto Regression model. Results shows that the TQWT in soft thresholding mode can attenuate random noise far better than TQWT in hard thresholding mode and Autoregressive-FX method.
基金supported by the National Natural Science Foundation of China (Grant No. 51275384)the Key Project of National Natural Science Foundation of China (Grant No. 51035007)+1 种基金the Important National Science and Technology Specific Projects (Grant No. 2010ZX04014-016)the National Basic Research Program of China ("973" Program) (Grant No. 2009CB724405)
文摘Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing the difficulty of the feature extraction.Thereby,a novel denoising method based on the tunable Q-factor wavelet transform(TQWT)using neighboring coefficients is proposed in this article.The emerging TQWT possesses excellent properties compared with the conventional constant-Q wavelet transforms,which can tune Q-factor according to the oscillatory behavior of the signal.Meanwhile,neighboring coefficients denoising is adopted to avoid the overkill of conventional term-by-term thresholding techniques.Because of having the combined advantages of the two methods,the presented denoising method is more practical and effective than other methods.The proposed method is applied to a simulated signal,a rolling element bearing with an outer race defect from antenna transmission chain and a gearbox fault detection case.The processing results demonstrate that the proposed method can successfully identify the fault features,showing that this method is more effective than the conventional wavelet thresholding denoising methods,term-by-term TQWT denoising schemes and spectral kurtosis.
基金the National Natural Science Foundation of China(U19B2031).
文摘Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wavelet transform(TQWT)for moving target detection.Firstly,this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform(FRFT)domain.Then,TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target’s oscillation characteristics,using the basis pursuit denoising(BPDN)algorithm to get the wavelet coefficients.Furthermore,an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target.Finally,experiments on the Council for Scientific and Industrial Research(CSIR)dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.
文摘This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT;similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.
