RCS feature extraction from simple targets using time-frequency analysis

1996 ◽  
Author(s):  
James L. Rasmussen ◽  
Randy L. Haupt ◽  
Michael J. Walker
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency

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.

An Investigation of Feature Extraction of Local Energy in Joint Time-Frequency Analysis Based on Process Systems Modeling and Simulation

2012 ◽  
Vol 198-199 ◽  
pp. 803-807
Author(s):  
Feng Li Wang ◽  
Shu Lin Duan ◽  
Hong Tao Gao
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Local Energy ◽  
Simulation Research ◽  
Time Frequency Analysis ◽  
Process Systems ◽  
Frequency Space ◽  
Time Frequency ◽  
Stationary Signals ◽  
Local Wave

Aiming at the characteristics of local properties of the non-stationary signals, a noval feature extraction approach based on the local energy in joint time-frequency analysis is proposed. The concept of local energy in joint time- frequency analysis based on local wave analysis was used to measure the signal energy in time-frequency space of the signal. Firstly, analyze the signal with local wave method and then make Hilbert transformation of it. Then partition several areas in time frequency space and compute its local energy. From the expression of local wave time-frequency distributing, not only total energy of signal can be computed but also local energy in time-frequency space. Simulation research indicates that the developed approach was effective.

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.

Sign in / Sign up

Export Citation Format

Share Document