Blind channel equalization algorithm based on dual unscented Kalman filter for chaotic multi-input multi-output communication systems

Abstract

To reduce channel noise, fading, and inter-user interference effectively in the chaotic communication systems with multi-user, a blind channel equalization algorithm based on dual unscented Kalman filter algorithm is proposed. Assuming that the coefficients of a multi-input multi-output (MIMO) channel can be described by an autoregressive model, two separate state-space representations are used for the signals and coefficients. Then two unscented Kalman filters are used to estimate chaotic signals and channel coefficients simultaneously. The simulation results indicate that the algorithm can effectively track the coefficients of the multi-path fading channel in chaotic MIMO communication systems at a fast convergence speed.

Keywords

chaotic MIMO system / blind channel equalization / unscented Kalman filter

References

[1] Pecora L. M., Carroll T. L. Synchronization in chaotic system[J]. Physical Review Letters, 1990, 64(8): 821-824.

[2] Wang S. Y., Feng J. C., Xie S. L. A multiuser chaotic communication scheme by parameter division multiple access[J]. Circuits, Systems and Signal Processing, 2007, 26(6): 839-852.

[3] Feng J. C., Tse C. K. Reconstruction of Chaotic Signals with Applications to Chaos-Based Communications[M]. 2007, Beijing: Tsinghua University Press.

[4] Li K. Z., Zhao M. C., Fu X. C. Projective synchronization of driving-response systems and its application to secure communication[J]. IEEE Transactions on Circuits and Systems (Part-I), 2009, 56(10): 2280-2291.

[5] Zhu Z. W., Leung H. Adaptive blind equalization for chaotic communication systems using extended-Kalman filter[J]. IEEE Transactions on Circuits and Systems (Part-I), 2001, 48(8): 979-989.

[6] Tsai J. S. H., Lu F. C., Provence R. S., et al. A new approach for adaptive blind equalization of chaotic communication: The optimal linearization technique[J]. Computers and Mathematics with Applications, 2009, 58(9): 1687-1698.

[7] Vural C., Cetinel G. Blind equalization of single-input single-output fir channels for chaotic communication sys tems[J]. Digital Signal Processing, 2010, 20(1): 201-211.

[8] Wang S. Y., Feng J. C. Blind channel equalization algorithm for chaotic communication systems with multiuser[J]. Journal of Jilin University (Engineering and Technology Edition), 2008, 38(6): 1469-1473.

[9] Feng J. C., Xie S. L. A noise reduction method for noisy contaminated chaotic signal[C]. International Conference on Communications, Circuits and Systems, 2005, Hong Kong, China: IEEE Press 1173-1176.

[10] Hu Z. H., Feng J. C. Blind extraction of chaotic signals from convolutive mixture using unscented Kalman filter [J]. Journal of Southwest University (Natural Science Edition), 2010, 32(7): 146-150.

[11] Julier S. J., Uhlmann J. K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401-422.

[12] Hao Y. L., Xiong Z. L., Sun F., et al. Comparison of unscented Kalman filters[C]. IEEE International Conference on Mechatronics and Automation, 2007, Harbin, China: IEEE Press 895-899.

[13] Julier S. J., Uhlmann J. K., Durrant-Whyte H. F. A new approach for filtering nonlinear systems[C]. Proceedings of the 1995 American Control Conference, 1995, Seattle, WA, USA: IEEE Presss 1628-1632.

[14] Hu Z. H., Feng J. C. Chaotic communications with multiuser based on unscented Kalman filter [J]. Acta Physica Sinica, 2011, 60(7): 070505

[15] Wang S. Y., Feng J. C. Chaotic communication scheme by parameter division multiple access[J]. Acta Electronica Sinica, 2007, 35(7): 1251-1256.