Matching Pursuit Distribution And Minimum Cross Entropy Technique

Subject Areas : Communication

صدیقه غفرانی ¹ (Islamic Azad University, South Tehran Branch)
احمد آیت اللهی ² (Iran, University of Science and Technology)

Received: 2010-04-08 Accepted : 2010-06-09 Published : 2010-03-01

Keywords: Matching Pursuit Decomposition, Gaussian Atom, Removing Noise, Minimum Cross Entropy Technique,

Abstract :

The traditional method for studying non-stationary signals is spectrogram based on the short-time Fourier transform (STFT). The well known limitation of the STFT is the inherent trade-off between time and frequency resolution. The Wigner-Ville (WV) distribution has the best time-frequency resolution, but its draw back is generating cross-terms. The matching pursuit (MP) distribution based on using the Gaussian atom is always positive, does not include cross-term, and has convenient resolution. In this paper, we have shown in addition to the known properties, the MP distribution can also remove the additive noise inherently. On the other words, we are able to remove the noise just by limiting the algorithm iterations and without paying any additional cost. Although the MP distribution based on using the Gaussian atoms is always positive and it has convenient resolution, according to the MP the time marginal and the frequency marginal will not be obtained accurately. In this paper, it has been shown that by implementing the minimum cross entropy (MCE) technique according to the MP distribution as a priory positive distribution, the new extracted distribution has the most similarity to the MP distribution and it also satisfies the correct time and frequency marginal.