Back to Current Issues

Non-Stationary Signal Analysis A Modified Time Frequency Approach

Devika, Monalisa Nayak, Kodanda Dhar Sa, Dillip Dash

Department of Electronics and Tele Communication, Indira Gandhi Institute of Technology, Odisha, India
:10.22362/ijcert/2017/v4/i7/xxxx [UNDER PROCESS]

Fourier investigation becomes invaluable when the signal contains non-stationary characteristics or transitory characteristics like transients and patterns that vary with time. As time domain and frequency domain representations are inadequate to give all the information possessed by the non-stationary signal. Therefore time-Frequency methods (TFMs) are used to analyze a signal in time and frequency domains simultaneously. This paper deals with the analysis of non-stationary signals by using short time Fourier transform; fractional Fourier transform to analyze the time frequency behavior of the non-stationary signal. A combination method is known as Short time fractional Fourier transform (STFRFT) also proposed here, which provides unique properties of the non-stationary signal. By using different windows like the rectangular window, Hamming window, Hanning window and Blackman window, the fractional Fourier transform of the chirp signal has been plotted. The MATLAB simulations were made to show the STFRFT of the signal.

Devika, Monalisa Nayak,Kodanda Dhar Sa, Dillip Dash. (2017).Non-Stationary Signal Analysis A Modified Time Frequency Approach. International Journal of Computer Engineering In Research Trends, 4(7), 313-318. Retrieved from

Keywords : Fourier Transform, FRFT, Non-Stationary signals, STFT, Window function

[1] Qi L, Tao R, Zhou S, Wang Y., Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform. Science in China series F: information sciences, 2004, 47(2), pp. 184-98.
[2] Claasen T, Mecklenbräuker W., TIME-FREQUENCY SIGNAL ANALYSIS. Philips Journal of Research. 1980, 35(4/5), pp. 276-300.
[3] Rao P, Taylor F. Estimation of instantaneous frequency using the discrete Wigner distribution. Electronics letters, 1990,126 (4), pp. 246-8.
[4] Choi H, Williams W., Improved time-frequency representation of multicomponent signals using exponential kernels. IEEE Transactions on Acoustics, Speech, and Signal  Processing, 1989, 37(6), pp. 862-71.
[5] Hossen AN, Heute U, Shentov OV, Mitra SK. Subband DFT—part II: accuracy, complexity and applications. Signal Processing, 1995,41(3), pp. 279-94.
[6] Hammond JK, White PR., The analysis of non-stationary signals using time-frequency methods. Journal of Sound and Vibration. 1996, 190(3), pp. 419-47.
[7] Chen VC, Ling H., Time-frequency transforms for radar imaging and signal analysis.  Artech House, 2002.
[8] Narayanan VA, Prabhu KM.,  The fractional Fourier transform: theory, implementation and error analysis. Microprocessors and Microsystems, 2003, 27(10), pp. 511-21.
[9] Qu H, Wang R, Qu W, Zhao P.,  Research on DOA Estimation of Multi-Component LFM Signals Based on the FRFT. Wireless Sensor Network, 2009, 1(3), pp. 171-81.
[10] Sun HB, Liu GS, Gu H, Su WM, Application of the fractional Fourier transform to  moving target detection in airborne SAR, IEEE Transactions on Aerospace and  Electronic Systems, 2002, 38(4), pp. 1416-24.
[11] Tao R, Zhang F, Wang Y., Fractional power spectrum. IEEE Transactions on Signal Processing. 2008, 56(9), pp. 4199-206.
[12] Podder P, Khan TZ, Khan MH, Rahman MM. Comparative performance analysis of hamming, hanning and blackman window. International Journal of Computer  Applications, 2014,96(18), pp. 2001-2006.

DOI Link : Not yet assigned

Download :

Refbacks : There are currently no refbacks

Quick Links


Science Central

Score: 13.30

Submit your paper to