Novel Approach To Cross-Terms Elimination In Wigner Distribution Using Adaptive Filtering In Frequency Domain

1989 ◽  
Author(s):  
Moeness G. Amin ◽  
Fred Allen
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Wigner Distribution ◽  
Novel Approach ◽  
Cross Terms

Signature Features in Returned Echoes From Submerged Targets Insonified by Short, Broadband Pulses: Comparison of Experiments and Theory

1995 ◽  
Author(s):  
Torbjörn Ståhlsten ◽  
Hans C. Strifors ◽  
Guillermo C. Gaunaurd
Keyword(s):  
Experimental Data ◽  
Theoretical Prediction ◽  
Frequency Domain ◽  
Time Window ◽  
Wigner Distribution ◽  
Time Frequency ◽  
Comparison Of Experiments ◽  
Cross Terms ◽  
Sound Pulses ◽  
Broadband Sound

Abstract Backscattered echoes are studied from submerged elastic targets in the frequency domain and combined time-frequency domain when the targets are insonified by short, broadband sound pulses. The targets are either an air-filled spherical shell or various solid brass or steel spheres. The incident waveform is generated by weighting a sinusoidal signal with a Blackman time-window of a few cycles width. The spectrum is computed from each recorded set of experimental data and the result is shown to agree well with the theoretical prediction for the corresponding target and interrogating waveform. An advantage of the time-frequency approach is that target signatures can show the time evolution of the resonance features that identify each target. Experimentally obtained data are processed in the time-frequency domain using a pseudo-Wigner distribution (PWD). The associated time-window is Gaussian, and its width is adjusted to suppress the interference of cross-terms in the PWD, yet retaining the desired property of time-frequency concentrating the extracted features.

Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2831-2847 ◽  
Author(s):  
Y Zhou ◽  
J Chen ◽  
G M Dong ◽  
W B Xiao ◽  
Z Y Wang
Keyword(s):  
Fault Diagnosis ◽  
Spectral Density ◽  
Wigner Distribution ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Accurate Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Cross Terms

The vibration signals of rolling element bearings are random cyclostationary when they have faults. Also, statistical properties of the signals change periodically with time. The accurate analysis of time-varying signals is an essential pre-requisite for the fault diagnosis and hence safe operation of rolling element bearings. The Wigner distribution is probably most widely used among the Cohen’s class in order to describe how the spectral content of a signal changes over time. However, the basic nature of such signals causes significant interfering cross-terms, which do not permit a straightforward interpretation of the energy distribution. To overcome this difficulty, the Wigner–Ville distribution (WVD) based on the cyclic spectral density (CSD) is discussed in this article. It is shown that the improved WVD, based on CSD of a long time series, can render the time–frequency distribution less susceptible to noise, and restrain the cross-terms in the time–frequency domain. Simulation and experiment of the rolling element-bearing fault diagnosis are performed, and the results indicate the validity of WVD based on CSD in time–frequency analysis for bearing fault detection.

Global Image Dehazing via Frequency Perception Filtering

2019 ◽  
Vol 28 (09) ◽  
pp. 1950142
Author(s):  
Linli Xu ◽  
Jing Han ◽  
Tian Wang ◽  
Lianfa Bai
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Low Frequency ◽  
Intermediate Frequency ◽  
Human Visual Perception ◽  
Image Dehazing ◽  
Weighting Factors ◽  
Outdoor Scenes ◽  
Frequency Domain Filtering ◽  
Three Components

In outdoor scenes, haze limits the visibility of images, and degrades people’s judgement of the objects. In this paper, based on an assumption of human visual perception in frequency domain, a novel image haze removal filtering is proposed. Combining this assumption with the theory of frequency domain filtering, we first estimate the cut-off frequency to divide the frequency domain of the hazy image into three components — low-frequency domain, intermediate-frequency domain and high-frequency domain. Then, by introducing the weighting factors, the three components are recombined together. After the theoretical deduction of frequency domain, the establishment of the actual model and adjusting the cut-off frequency and weighting factors, we finally acquire a global and adaptive filtering. This filtering can restore the details and the contours of the images, which have less noise, and improve the visibility of the objects in hazy images. Moreover, our method is simple in structure and strongly applicable, and rarely affected by parameters. Our algorithm is stable and performs well in heavy fog and the scene changes.

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