Volume : VI, Issue : I, January - 2017

Radar signal Analysis Using EMD and CEEMD

G. Madhavilatha, Dr. S. Varadarajan, Dr. P. Satishkumar

Abstract :

 Data analysis is an vital part in pure research and practical applications.. Basically it‘s defined as a method of evaluating data using analytical and logical reasoning to examine every component of the information provided. It is accepted proven fact that linear and stationary processes are easy to analyze through (TF) representation methods of time domain signal like Fourier transform (FT), Short Time Fourier transform (STFT), wavelet transform etc., however the real world signals are mostly non–linear and non–stationary in nature. Analysis of such time varied signals isn‘t an easy method. Breaking out a complex method into separate components is named decomposition. Hilbert Huang Transform (HHT) is used for processing non–stationary and nonlinear signals. HHT is one among the time– a frequency analysis technique that consists of 2 parts: Empirical Mode Decomposition (EMD) and instantaneous frequency solution. However, EMD experiences some issues, like “mode mixing”. To Solve these issues, a and new technique was projected, the Ensemble Empirical Mode Decomposition (EEMD). EEMD relies on averaging the modes obtained by EMD applied to many realizations of Gaussian white noise superimposed to the original signal. The resulting decomposition solves the EMD mode mixing problem, but it introduces new ones. The EMD properties like completeness and fully data driven number of modes are lost by EEMD. To resolve this drawback, a and new technique known as Complete Ensemble Empirical Mode Decomposition (CEEMD) technique is introduced. This technique depends on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise superimposed to original signal. Its decomposition is complete with numerically negligible errors. It will solve mode mixing problems in EMD and improve resolution of EEMD when the signal has low signal to noise (SNR) ratio. It provides higher spectral separation of the modes and a lesser number of sifting iterations, reducing the computational price.

Keywords :

Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

G.Madhavilatha, Dr. S.Varadarajan, Dr.P.Satishkumar, Radar signal Analysis Using EMD and CEEMD, International Journal of Scientific Research, Volume : 6 | Issue : 1 | JANUARY 2017

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