An Optimal Radar Signal Processor in Short Time Fractional Fourier Transform

2017 ◽  
Vol 14 (6) ◽  
pp. 2791-2801 ◽  
Author(s):  
P. Jaisi Praba ◽  
S. S Vinsley
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Radar Signal ◽  
Short Time ◽  
Signal Processor

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

scholarly journals Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1477 ◽  
Author(s):  
Xinqun Liu ◽  
Tao Li ◽  
Xiaolei Fan ◽  
Zengping Chen
Keyword(s):  
Fourier Transform ◽  
Random Sample ◽  
Fractional Fourier Transform ◽  
Estimation Method ◽  
Ultra Wideband ◽  
Estimation Methods ◽  
Radar Signal ◽  
Chirp Rate ◽  
Time Frequency ◽  
Random Sample Consensus

The Nyquist folding receiver (NYFR) can achieve a high-probability interception of an ultra-wideband (UWB) signal with fewer devices, while the output of the NYFR is converted into a hybrid modulated signal of the local oscillator (LO) and the received signal, which requires the matching parameter estimation methods. The linear frequency modulation (LFM) signal is a typical low probability of intercept (LPI) radar signal. In this paper, an estimation method of both the Nyquist Zone (NZ) index and the chirp rate for the LFM signal intercepted by NYFR was proposed. First, according to the time-frequency characteristics of the LFM signal, the accurate NZ and the rough chirp rate was estimated based on least squares (LS) and random sample consensus (RANSAC). Then, the information of the LO was removed from the hybrid modulated signal by the known NZ, and the precise chirp rate was obtained by using the fractional Fourier transform (FrFT). Moreover, a fast search method of FrFT optimal order was presented, which could obviously reduce the computational complexity. The simulation demonstrated that the proposed method could precisely estimate the parameters of the hybrid modulated output signal of the NYFR.

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.

scholarly journals Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform

2015 ◽  
Vol 4 (4) ◽  
pp. 531 ◽  
Author(s):  
Ashraf Adamu Ahmad ◽  
Abdullahi Daniyan ◽  
David Ocholi Gabriel
Keyword(s):  
Fourier Transform ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Side Lobe ◽  
Radar Signal ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Window Functions ◽  
Pulse Analysis ◽  
Short Time

The electronic intelligence (ELINT) system is used by the military to detect, extract information and classify incoming radar signals. This work utilizes short time Fourier transform (STFT) - time frequency distribution (TFD) for inter-pulse analysis of the radar signal in order to estimate basic radar signal time parameters (pulse width and pulse repetition period). Four well-known windows functions of different and unique characteristics were used for the localization of STFT to determine their various effects on the analysis. The window functions are Hamming, Hanning, Bartlett and Blackman window functions. Monte Carlo simulation is carried out to determine the performance of the signal analysis in presence of additive white Gaussian noise (AWGN). Results show that the lower the transition of main lobe width and higher the peak side lobe, the better the performance of the window function irrespective of time parameter being estimated. This is because 100 percent probability of correct estimation is achieved at signal to noise ratio of about -2dB for Bartlett, 4dB for both Hamming and Hanning, and 9dB for Blackman.

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.

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