Radar micro-doppler signal detection and extraction via short-time sparse fractional Fourier transform

2017 ◽  
Author(s):  
Xiaolong Chen ◽  
Yong Huang ◽  
Jian Guan ◽  
You He
Keyword(s):  
Fourier Transform ◽  
Signal Detection ◽  
Fractional Fourier Transform ◽  
Doppler Signal ◽  
Short 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.

Novel Short-Time Fractional Fourier Transform: Theory, Implementation, and Applications

2020 ◽  
Vol 68 ◽  
pp. 3280-3295 ◽  
Author(s):  
Jun Shi ◽  
Jiabin Zheng ◽  
Xiaoping Liu ◽  
Wei Xiang ◽  
Qinyu Zhang
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Short Time

A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset

2020 ◽  
pp. 42-66
Author(s):  
Dinesh Bhatia ◽  
Animesh Mishra
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Mapping Technique ◽  
Self Organizing Maps ◽  
Map Generation ◽  
Unsupervised Training ◽  
Cardio Vascular ◽  
Short Time

The role of ECG analysis in the diagnosis of cardio-vascular ailments has been significant in recent times. Although effective, the present computational algorithms lack accuracy, and no technique till date is capable of predicting the onset of a CVD condition with precision. In this chapter, the authors attempt to formulate a novel mapping technique based on feature extraction using fractional Fourier transform (FrFT) and map generation using self-organizing maps (SOM). FrFT feature extraction from the ECG data has been performed in a manner reminiscent of short time Fourier transform (STFT). Results show capability to generate maps from the isolated ECG wavetrains with better prediction capability to ascertain the onset of CVDs, which is not possible using conventional algorithms. Promising results provide the ability to visualize the data in a time evolution manner with the help of maps and histograms to predict onset of different CVD conditions and the ability to generate the required output with unsupervised training helping in greater generalization than previous reported techniques.

Short time coupled fractional fourier transform and the uncertainty principle

2021 ◽  
Vol 24 (3) ◽  
pp. 667-688
Author(s):  
Ramanathan Kamalakkannan ◽  
Rajakumar Roopkumar ◽  
Ahmed Zayed
Keyword(s):  
Fourier Transform ◽  
Time Delay ◽  
Frequency Shift ◽  
Uncertainty Principle ◽  
Fractional Fourier Transform ◽  
Short Time ◽  
Addition Formulas

Abstract In this paper, we introduce a short-time coupled fractional Fourier transform (scfrft) using the kernel of the coupled fractional Fourier transform (cfrft). We then prove that it satisfies Parseval’s relation, derive its inversion and addition formulas, and characterize its range on ℒ 2(ℝ2). We also study its time delay and frequency shift properties and conclude the article by a derivation of an uncertainty principle for both the coupled fractional Fourier transform and short-time coupled fractional Fourier transform.

Sign in / Sign up

Export Citation Format

Share Document