TY - JOUR
T1 - A new approach for adaptive blind equalization of chaotic communication
T2 - The optimal linearization technique
AU - Tsai, J. S.H.
AU - Lu, F. C.
AU - Provence, R. S.
AU - Shieh, L. S.
AU - Han, Z.
N1 - Funding Information:
This work was supported by the National Science Council of the Republic of China under contract NSC96-2221-E-006-292-MY3, the U.S. Army Research Office under grant W911NF-06-1-0507, and the National Science Foundation under grants NSF 0717860 and CNS-0910461.
PY - 2009/11
Y1 - 2009/11
N2 - Together with the optimal linearization technique, a blind-channel equalization for the extended-Kalman-filter-based chaotic communication is proposed in this paper. First, the optimal linearization technique is utilized to find the exact linear models of the chaotic system at operating states of interest. The proposed blind-channel equalization is formulated as a mixed nonlinear parameter and state estimation problem by an autoregressive (AR) model. The channel coefficients of a fading and multipath channel can be represented by an AR process. Then, an extended Kalman filter algorithm is utilized to reduce the effect of channel noise. By using the extended Kalman filter, the channel coefficients and the state of the system, which is the signal before going through the channel, can be estimated. The stability problem of the proposed blind-channel equalization is also addressed. Numerical examples and simulations are given to show the effectiveness and speed of convergence for the proposed methodology.
AB - Together with the optimal linearization technique, a blind-channel equalization for the extended-Kalman-filter-based chaotic communication is proposed in this paper. First, the optimal linearization technique is utilized to find the exact linear models of the chaotic system at operating states of interest. The proposed blind-channel equalization is formulated as a mixed nonlinear parameter and state estimation problem by an autoregressive (AR) model. The channel coefficients of a fading and multipath channel can be represented by an AR process. Then, an extended Kalman filter algorithm is utilized to reduce the effect of channel noise. By using the extended Kalman filter, the channel coefficients and the state of the system, which is the signal before going through the channel, can be estimated. The stability problem of the proposed blind-channel equalization is also addressed. Numerical examples and simulations are given to show the effectiveness and speed of convergence for the proposed methodology.
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U2 - 10.1016/j.camwa.2009.06.054
DO - 10.1016/j.camwa.2009.06.054
M3 - Article
AN - SCOPUS:70349096362
VL - 58
SP - 1687
EP - 1698
JO - Computers and Mathematics with Applications
JF - Computers and Mathematics with Applications
SN - 0898-1221
IS - 9
ER -