A Method of Radar Signal Feature Extraction Based on Fractional Fourier Transform

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.

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

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering - Advances in Healthcare Information Systems and Administration ◽

10.4018/978-1-7998-2120-5.ch003 ◽

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.

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An Optimal Radar Signal Processor in Short Time Fractional Fourier Transform

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2017.6577 ◽

2017 ◽

Vol 14 (6) ◽

pp. 2791-2801 ◽

Cited By ~ 1

Author(s):

P. Jaisi Praba ◽

S. S Vinsley

Keyword(s):

Fourier Transform ◽

Fractional Fourier Transform ◽

Radar Signal ◽

Short Time ◽

Signal Processor

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Feature extraction using HHT-based locally optimized short-time fractional Fourier transform for speaker recognition

2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR) ◽

10.1109/icivpr.2017.7890873 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jinfang Wang ◽

Hailong Du ◽

Ming Guo ◽

Xinli Nie ◽

Shuxin Luan ◽

...

Keyword(s):

Feature Extraction ◽

Fourier Transform ◽

Speaker Recognition ◽

Fractional Fourier Transform ◽

Short Time

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Compressed Sensing Reconstruction of Radar Echo Signal Based on Fractional Fourier Transform and Improved Fast Iterative Shrinkage-Thresholding Algorithm

Wireless Communications and Mobile Computing ◽

10.1155/2021/2272933 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Rui Zhang ◽

Chen Meng ◽

Cheng Wang ◽

Qiang Wang

Keyword(s):

Fourier Transform ◽

Compressed Sensing ◽

Fractional Fourier Transform ◽

Radar Signal ◽

Reconstruction Algorithms ◽

Echo Signal ◽

Time Frequency ◽

Iterative Shrinkage ◽

Radar Echo ◽

Iterative Shrinkage Thresholding

The compressed sensing theory, which has received great attention in the field of radar technology, can effectively reduce the data rate of high-resolution radar imaging systems and solve the problem of collecting, storing, and transmitting large amounts of data in radar systems. Through the study of radar signal processing theory, it can be found that the echo of radar LFM transmit signal has sparse characteristics in the distance upward; based on this, we can consider using the theory of compressed sensing in the processing of radar echo to optimize the processing. In this paper, a fast iterative shrinkage-thresholding reconstruction algorithm based on protection coefficients is proposed. Under the new scheme, firstly, the LFM echo signal’s good sparse representation is obtained by using the time-frequency sparse characteristics of the LFM echo signal under the fractional Fourier transform; all reconstruction coefficients are analyzed in the iterative process. Then, the coefficients related to the feature will be protected from threshold shrinkage to reduce information loss. Finally, the effectiveness of the proposed method is verified through simulation experiments and application example analysis. The experimental results show that the reconstruction error of this method is lower and the reconstruction effect is better compared with the existing reconstruction algorithms.

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