Partially blind source separation of continuous chaotic signals from linear mixture

The authors consider the problem of partially blind source separation of continuous chaotic signals from a linear mixture, in which the generating systems of chaotic signals are assumed to be available and the mixture matrix is unknown. Determination of the mixture matrix is firstly formulated as a problem of the synchronisation-based parameter estimation. Then an efficient parameter estimation method by exploiting the generating dynamics of chaotic signals is introduced. The estimated mixture matrix is used to design a signal separator to blindly separate the mixed chaotic signals. Three examples are given to illustrate the applicability of the proposed approach for the mixed chaotic signals generated by different and/or same dynamical systems and contaminated in measurement noise. In comparison with statistics-based methods, the new approach can solve the magnitude scaling indeterminacy and shows the separability of the mixed signals in strong noise background.

[1]  Kenneth J. Pope,et al.  Blind Signal Separation II. Linear, Convolutive Combinations: II. Linear, Convolutive Combinations , 1996, Digit. Signal Process..

[2]  Alan V. Oppenheim,et al.  Circuit implementation of synchronized chaos with applications to communications. , 1993, Physical review letters.

[3]  Xiaohua Zhu,et al.  Principles of Chaotic Signal Radar , 2007, Int. J. Bifurc. Chaos.

[4]  Kenneth J. Pope,et al.  Blind Signal Separation I. Linear, Instantaneous Combinations: I. Linear, Instantaneous Combinations , 1996, Digit. Signal Process..

[5]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[6]  Parlitz,et al.  Estimating model parameters from time series by autosynchronization. , 1996, Physical review letters.

[7]  Alexander S. Dmitriev,et al.  Separation of chaotic signal sum into components in the presence of noise , 2003 .

[8]  L. Tsimring,et al.  Multiplexing chaotic signals using synchronization , 1996 .

[9]  Wei Xing Zheng,et al.  Blind extraction of chaotic signal from an instantaneous linear mixture , 2006, IEEE Trans. Circuits Syst. II Express Briefs.

[10]  Young-Jai Park,et al.  Anti-synchronization of chaotic oscillators , 2003 .

[11]  Henry Leung,et al.  Separation of a mixture of chaotic signals , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[12]  Bing Lam Luk,et al.  Adaptive simulated annealing for optimization in signal processing applications , 1999, Signal Process..

[13]  E. Oja,et al.  Independent Component Analysis , 2013 .

[14]  Debin Huang Adaptive-feedback control algorithm. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Peng,et al.  Synchronizing hyperchaos with a scalar transmitted signal. , 1996, Physical review letters.

[16]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[17]  Douglas B. Williams,et al.  An optimal estimation algorithm for multiuser chaotic communications systems , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).