Extracting of Micro Doppler Parameter of Helicopter Rotor Based on Time-Frequency Analysis

2011 ◽  
Vol 130-134 ◽  
pp. 2696-2700 ◽  
Author(s):  
Lei Zhang ◽  
Guo Qing Huang
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Good Method ◽  
Helicopter Rotor ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Radar Echo ◽  
Doppler Parameter

The micro Doppler effect of the radar echo signal of helicopter rotor is studied, and the formula of helicopter rotor echo is obtained. Then the received echo signal of helicopter rotor simulated is analyzed in time domain, frequency domain and time-frequency domain respectively, the analysis results show that it is a good method to extract micro Doppler of helicopter rotor echo by time-frequency analysis. According to analysis results, obtained a method to determine parity of blades and velocity of helicopter rotor, these methods can be used to identify helicopter.

The Applied Research of Time-Frequency Analysis Method Employed in Seismic Signal Processing

2014 ◽  
Vol 955-959 ◽  
pp. 1809-1812
Author(s):  
Zuo Ju Wu ◽  
Zhi Jia Wang ◽  
Jun Wei Bi
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Seismic Signal ◽  
Frequency Domain Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Seismic Signal Processing

In the traditional processing of seismic signal, the frequency domain analysis method is always available to research some features which always vary with frequency. However, the condition of parameters which vary with time going can’t be considered in this method. So all the information in time domain have been neglected. In this article, time-frequency analysis method called HHT(Hilbert-Huang transform) is applied to analyze the Qingping wave of Wenchuan earthquake meticulously, which is the most advantaged to dissect the change features of the seismic record at different scales. Then we can get the dual properties in time domain and the frequency domain, such as the IMF function of each modal and the instantaneous frequency. For reflecting the time-frequency characteristics exactly and clearly, the Hilbert spectrum has been used to show these messages in the time-frequency plane.

Time Frequency Domain Analysis of the Dispersion of Guided Wave Modes

2004 ◽  
Author(s):  
Youn-Ho Cho ◽  
Yong-Kwon Kim ◽  
Ik-Keun Park
Keyword(s):  
Wavelet Transform ◽  
Group Velocity ◽  
Frequency Analysis ◽  
Time Domain ◽  
Excitation Frequency ◽  
Guided Wave ◽  
Time Frequency Analysis ◽  
Mode Identification ◽  
Time Frequency ◽  
Wave Modes

One of unique characteristics of guided waves is a dispersive behavior that guided wave velocity changes with an excitation frequency and mode. In practical applications of guided wave techniques, it is very important to identify propagating modes in a time-domain waveform for determination of defect location and size. Mode identification can be done by measurement of group velocity in a time-domain waveform. Thus, it is preferred to generate a single or less dispersive mode. But, in many cases, it is difficult to distinguish a mode clearly in a time-domain waveform because of superposition of multi modes and mode conversion phenomena. Time-frequency analysis is used as efficient methods to identify modes by presenting wave energy distribution in a time-frequency. In this study, experimental guided wave mode identification is carried out in a steel plate using time-frequency analysis methods such as wavelet transform. The results are compared with theoretically calculated group velocity dispersion curves. The results are in good agreement with analytical predictions and show the effectiveness of using the wavelet transform method to identify and measure the amplitudes of individual guided wave modes.

scholarly journals Application of S-Transform in ISAR Imaging

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 676 ◽  
Author(s):  
Bo Zang ◽  
Mingzhe Zhu ◽  
Xianda Zhou ◽  
Lu Zhong
Keyword(s):  
Frequency Analysis ◽  
Low Frequency ◽  
Frequency Region ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Isar Imaging ◽  
Body Components ◽  
Over Time

In inverse synthetic aperture radar (ISAR) imaging, time-frequency analysis is the basic method for processing echo signals, which are reflected by the results of time-frequency analysis as each component changes over time. In the time-frequency map, a target’s rigid body components will appear as a series of single-frequency signals in the low-frequency region, and the micro-Doppler components generated by the target’s moving parts will be distributed in the high-frequency region with obvious frequency modulation. Among various time-frequency analysis methods, S-transform is especially suitable for analyzing these radar echo signals with micro-Doppler (m-D) components because of its multiresolution characteristics. In this paper, S-transform and the corresponding synchrosqueezing method are used to analyze the ISAR echo signal and perform imaging. Synchrosqueezing is a post-processing method for the time-frequency analysis result, which could retain most merits of S-transform while significantly improving the readability of the S-transformation result. The results of various simulations and actual data will show that S-transform is highly matched with the echo signal for ISAR imaging: the better frequency-domain resolution at low frequencies can concentrate the energy of the rigid body components in the low-frequency region, and better time resolution at high frequencies can better describe the transformation of the m-D component over time. The combination with synchrosqueezing also significantly improves the effect of time-frequency analysis and final imaging, and alleviates the shortcomings of the original S-transform. These results will be able to play a role in subsequent work like feature extraction and parameter estimation.

scholarly journals Clinical usefulness and feasibility of time-frequency analysis of chemosensory event-related potentials

2013 ◽  
Vol 51 (3) ◽  
pp. 210-221
Author(s):  
C. Huart ◽  
Ph Rombaux ◽  
T. Hummel ◽  
A. Mouraux
Keyword(s):  
Frequency Analysis ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Discrimination Performance ◽  
Clinical Usefulness ◽  
Olfactory Stimulation ◽  
Signal To Noise ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Time Domain Averaging

Background: The clinical usefulness of olfactory event-related brain potentials (OERPs) to assess olfactory function is limited by the relatively low signal-to-noise ratio of the responses identified using conventional time-domain averaging. Recently, it was shown that time-frequency analysis of the obtained EEG signals can markedly improve the signal-to-noise ratio of OERPs in healthy controls, because it enhances both phase-locked and non phase-locked EEG responses. The aim of the present study was to investigate the clinical usefulness of this approach and evaluate its feasibility in a clinical setting. Methodology: We retrospectively analysed EEG recordings obtained from 45 patients (15 anosmic, 15 hyposmic and 15 normos- mic). The responses to olfactory stimulation were analysed using conventional time-domain analysis and joint time-frequency analysis. The ability of the two methods to discriminate between anosmic, hyposmic and normosmic patients was assessed using a Receiver Operating Characteristic analysis. Results: The discrimination performance of OERPs identified using conventional time-domain averaging was poor. In contrast, the discrimination performance of the EEG response identified in the time-frequency domain was relatively high. Furthermore, we found a significant correlation between the magnitude of this response and the psychophysical olfactory score. Conclusion: Time-frequency analysis of the EEG responses to olfactory stimulation could be used as an effective and reliable diagnostic tool for the objective clinical evaluation of olfactory function in patients.

Diagnosis of damaged wind turbine blade by noise characteristics

2020 ◽  
Vol 68 (2) ◽  
pp. 146-156
Author(s):  
Chao-Nan Wang ◽  
Tang-Yao Chi
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Frequency Analysis ◽  
Time Domain ◽  
Wind Turbine Blade ◽  
Statistical Regression ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Noise Characteristics

This study has proposed two estimation models of noise signal characteristic diagnosis based on time-domain and time-frequency analysis. The diagnosis of time domain was based on the fractal theory, and the result of fractal dimensions was converted into Gauss distribution, so as to provide a feature extraction for abnormality diagnosis of damaged blade. In addition, for time-frequency analysis, the wavelet methodwas used as the basis of signal analysis. The Morlet transform and mother wavelet were used for wavelet analysis of signal to obtain the result of time-frequency analysis. When the time axis was integrated, the marginal spectrum of frequency domain was obtained, and statistical regression analysis was used to provide another method of feature extraction diagnosis. The wind turbine blade signal was measured in actual wind turbine operation at Changhua Coastal Industrial Park for diagnostic analysis, so as to provide a multi-diagnostic model of wind turbine blade prewarning and health management models.

A Time-Frequency Analysis for Deflection of a Cutting Disc Used in Sawing Process

2011 ◽  
Vol 268-270 ◽  
pp. 847-852 ◽  
Author(s):  
Ismail Ucun ◽  
Fatih Onur Hocaoğlu ◽  
Sukru Gorgulu
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Data Sequence ◽  
Laser Device ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Components ◽  
Sawing Process ◽  
Lateral Deflections

Cutting discs are affected by various forces which cause stresses and deflections on the discs. Lateral deflection is one of the most frequent. It is important to know that major lateral deflections can cause undesired failures on the disc. In this study, time/frequency analysis of deflection on a cutting disc used in metal sawing process has been performed. The deflections are measured by using a laser device called KEYENCE and data recorded during the cutting process. The data sequence is converted into frequency domain with Fourier transform techniques and frequency components are analyzed. It is observed that some frequency components carry important information about the process. Using this information, the starting and ending times of cutting are determined and presented.

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