A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

Signal Feature Extraction about the Oil Whirl and Oil Oscillation Based on EMD

2011 ◽  
Vol 141 ◽  
pp. 483-487
Author(s):  
Bao Jie Xu ◽  
Ran Liu
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Oil Whirl ◽  
Adaptive Time

The article discusses the EMD and adaptive time-frequency analysis method based on EMD, and explains the characteristics about oil whirl, oil oscillation. Apply EMD and Hilbert-Huang t transformation to extract characteristics, which verifies applying EMD into feature extraction is effective.

Research on Joint Time-Frequency Analysis Methods of Adaptive Radar Signal

2014 ◽  
Vol 989-994 ◽  
pp. 4009-4013 ◽  
Author(s):  
Qiang Xing ◽  
Wei Gang Zhu ◽  
Yuan Bo ◽  
Kang Wang
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Radar Signal ◽  
Analysis Method ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Adaptive Radar ◽  
Short Time

Faced with complex electromagnetic environment and varieties of adaptive radar waveforms, radar signal analysis and identification becomes more and more complex. Considering two important physical quantities - time and frequency in modern signal processing methods, this paper proposes that the joint time-frequency analysis (JTFA) method based on fractional Fourier transform (FrFT) and short-time Fourier transform (STFT) is applied to adaptive radar signal processing. The simulation results show that the joint time-frequency analysis method is superior to single short-time Fourier transform, getting a better analysis of results. The joint time-frequency analysis method provides the joint distribution of the time domain and frequency domain for adaptive radar signal analysis and describes the relationship between signal frequency and time.

LFM Signal Detection Method Based on Fractional Fourier Transform

2014 ◽  
Vol 989-994 ◽  
pp. 4001-4004 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Yu Ming Zhu
Keyword(s):  
Fourier Transform ◽  
Signal Detection ◽  
Frequency Analysis ◽  
Experimental Analysis ◽  
Fractional Fourier Transform ◽  
Detection Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Strong Impulse

As a generalization of Fourier transform, the fractional Fourier Transform (FRFT) contains simultaneity the time-frequency information of the signal, and it is considered a new tool for time-frequency analysis. This paper discusses some steps of FRFT in signal detection based on the decomposition of FRFT. With the help of the property that a LFM signal can produce a strong impulse in the FRFT domain, the signal can be detected conveniently. Experimental analysis shows that the proposed method is effective in detecting LFM signals.

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