On-line blind source separation algorithm based on second order statistics

An on-line blind source separation （BSS） algorithm is presented in this paper under the assumption that sources are temporarily correlated signals. By using only some of the observed samples in a recursive calculation, the whitening matrix and the rotation matrix could be approximately obtained through the measurement of only one cost function. SimNations show goad performance of the algorithm.

Cauchy pdf modelling and its application to SAR image despeckling

Synthetic aperture radar （SAR） imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Therefore, how to restore SAR image from the speckle has become a necessary step in post-processing of image. A new despeckling method is putforth on the basis of wavelet. First, a new approach on the basis of ＂second kind statistics＂ is used to estimate the dispersion parameter of the Cauchy distribution. Then, this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image. Based on the above ideas, a new homomorphic wavelet-based maximum a posterior （MAP） despeckling method is proposed. Finally, the simulated speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention.

Blind Adaptive MMSE Equalization of Underwater Acoustic Channels Based on the Linear Prediction Method

The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.

BLIND SIGNAL SEPARATION OF LINEAR MIXTURE USING TRILINEAR DECOMPOSITION

This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint diagonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.