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.

scholarly journals Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform

2017 ◽  
Vol 3 (3) ◽  
pp. 146
Author(s):  
Yovinia Carmeneja Hoar Siki ◽  
Natalia Magdalena Rafu Mamulak
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Music Signal ◽  
Short Time

Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.

scholarly journals Area-Efficient Short-Time Fourier Transform Processor for Time–Frequency Analysis of Non-Stationary Signals

2020 ◽  
Vol 10 (20) ◽  
pp. 7208
Author(s):  
Hohyub Jeon ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Fourier Transform ◽  
Real Time ◽  
Frequency Analysis ◽  
Path Delay ◽  
Window Length ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Signals ◽  
Short Time

In this paper, we propose an area-efficient short-time Fourier transform (STFT) processor that can perform time–frequency analysis of non-stationary signals in real time, which is essential for voice or radar-signal processing systems. STFT processors consist of a windowing module and a fast Fourier transform processor. The length of the window function is related to the time–frequency resolution, and the required window length varies depending on the application. In addition, the window function needs to overlap the input data samples to minimize the data loss in the window boundary, and overlap ratios of 25%, 50%, and 75% are generally used. Therefore, the STFT processor should ideally support a variable window length and overlap ratio and be implemented with an efficient hardware architecture for real-time time–frequency analysis. The proposed STFT processor is based on the radix-4 multi-path delay commutator (R4MDC) pipeline architecture and supports a variable length of 16, 64, 256, and 1024 and overlap ratios of 25%, 50%, and 75%. Moreover, the proposed STFT processor can be implemented with very low complexity by having a relatively lower number of delay elements, which are the ones that increase complexity in the most STFT processors. The proposed STFT processor was designed using hardware description language (HDL) and synthesized to gate-level circuits using a standard cell library in a 65 nm CMOS process. The proposed STFT processor results in logic gates of 197,970, which is 63% less than that of the conventional radix-2 single-path delay feedback (R2SDF) based STFT processor.

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.

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