To solve the problem of blind source separation for chaotic signals, an improved blind separation algorithm is proposed. A function is constructed by signal separation evaluation index, which adaptively updates the step size and momentum factor, then substitutes the obtained variable step-size function into blind source separation algorithm and introduces the adaptive momentum item. Different from most algorithms which can not estimate the mixing matrix, the proposed algorithm estimates iteratively the mixing matrix by the variable step function, then the global matrix and the estimated evaluation can be obtained on which step and momentum factor are iteratively updated. Finally, the separation matrix is obtained. Simulations show that the algorithm is effective to adjust the step and momentum factor based on the estimated evaluation index constructor. In stationary and non-stationary environments, the algorithm has faster convergence speed and lower steady error for separating the mixed chaotic signals. When mixing color noise, the proposed algorithm is better than that of the traditional algorithm, which shows that the proposed algorithm has certain application value to the chaotic signal blind source separation processing.
YU Simin, LÜ Jinhu, and LI Chengqing. Some progresses of chaotic cipher and its applications in multimedia secure communications[J]. Journal of Electronics & Information Technology, 2016, 38(3): 735-752. doi: 10.11999/JEIT151356.
HUANG Yu, LIU Yufeng, PENG Zhimin, et al. Research on particle swarm optimization algorithm with characteristic of quantum parallel and its application in parameter estimation for fractional-order chaotic systems[J]. Acta Physica Sinica, 2015, 64(3): 228-235. doi: 10.7498/aps.64.030505.
WANG Shiyuan and FENG Jiuchao. A novel method of estimating parameter and its application to blind separation of chaotic signals[J]. Acta Physica Sinica, 2012, 61(17): 170508. doi: 05.45.-a,05.45.Vx,84.40.Ua.
CHEN Yue, LÜ Shanxiang, WANG Mengjiao, et al. A blind source separation method for chaotic signals based on artificial bee colony algorithm[J]. Acta Physica Sinica, 2015, 64(9): 090501. doi: 10.7498/aps.64.090501.
[6]
OU Shifeng, WANG Xianyun, and GAO Ying. Adaptive improved RLS algorithm for blind source separation[J]. Wydawnictwo SIGMA-NOT, 2013, 89(3b): 81-83.
OU Shifeng, GAO Ying, and ZHAO Xiaohui. Adaptive combination algorithm and its modified scheme for blind source separation [J]. Journal of Electronics & Information Technology, 2011, 33(5): 1243-1247. doi: 10.3724/SP.J.1146. 2010.00871.
OU Shifeng, GENG Chao, and GAO Ying. Momentum term based blind source separation algorithm and its performance modified strategies[J]. Acta Electronica Sinica, 2014, 42(1): 42-48. doi: 10.3969/j.issn.0372-2112.2014.01.007.
[10]
PAL M, ROY R, BASU J, et al. Blind source separation: A review and analysis[C]. Oriental Cocosda Held Jointly with 2013 Conference on Asian Spoken Language Research and Evaluation. Gurgaon, India, 2013: 1-5.
JI Ce, YANG Kun,TAO Yiming, et al. An adaptive variable step-size blind source separation algorithm in nonstationary environment[J]. Control and Decision, 2016, 31(4): 735-739. doi: 10.13195/j.kzyjc.2015.0170.
[12]
CARDOSO J F and LAHELD B H. Equivariant adaptive source separation[J]. IEEE Transactions on Signal Processing, 1997, 44(12): 3017-3030.
[13]
MKADEM F and BOUMAIZA S. Physically inspired neural network model for RF power amplifier behavioral modeling and digital predistortion[J]. IEEE Transactions on Microwave Theory & Techniques, 2011, 59(4): 913-923.
[14]
张贤达. 矩阵分析与应用[M]. 北京: 清华大学出版社, 2004: 310-314.
ZHANG Xianda. Matrix Analysis and Applications[M]. Beijing: Tsinghua University Press, 2004: 310-314.
[15]
XU P, SHEN Y, and SU Q. Blind source separation with variable step-size method based on a reference separation system[C]. IEEE International Conference on Signal Processing, Communications and Computing. Guilin, 2014: 110-114.
JI Ce, YANG Kun, WANG Yanru, et al. Variable step-size nonholonomic natural gradient algorithm based on sign operator[J]. Pattern Recogniton and Artificial Intelligence, 2014, 27(11): 1026-1031. doi: 10.16451/j.cnki.issn1003-6059. 2014.11.002.
XU Chengfa, HAO Yuxing, LU Lu, et al. Fast angle estimation algorithm based on cross-correlation[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1446-1451. doi: 10.11999/JEIT151021.