Variable-step Blind Source Separation Algorithm with Adaptive Momentum Item for Chaotic Signals

ZHANG Tianqi; MA Baoze; QIANG Xingzi; QUAN Shengrong

doi:10.11999/JEIT160578

Volume 39 Issue 4

Apr. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(4): 908-914

ZHANG Tianqi, MA Baoze, QIANG Xingzi, QUAN Shengrong. Variable-step Blind Source Separation Algorithm with Adaptive Momentum Item for Chaotic Signals[J]. Journal of Electronics & Information Technology, 2017, 39(4): 908-914. doi: 10.11999/JEIT160578

Citation:

ZHANG Tianqi, MA Baoze, QIANG Xingzi, QUAN Shengrong. Variable-step Blind Source Separation Algorithm with Adaptive Momentum Item for Chaotic Signals[J]. Journal of Electronics & Information Technology, 2017, 39(4): 908-914. doi: 10.11999/JEIT160578

Citation:

PDF( 700 KB)

Variable-step Blind Source Separation Algorithm with Adaptive Momentum Item for Chaotic Signals

doi: 10.11999/JEIT160578

Funds:

The National Natural Science Foundation of China (61671095, 61371164, 61275099), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), The Research Project of Chongqing Educational Commission (KJ130524, KJ1600427, KJ1600429)

Received Date: 2016-06-03
Rev Recd Date: 2016-11-28
Publish Date: 2017-04-19

Abstract

Abstract

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.
- Blind source separation,
- EASI algorithm,
- Chaotic signals,
- Momentum factor,
- Variable-step

FullText(HTML)

References(29)

References

禹思敏, 吕金虎, 李澄清. 混沌密码及其在多媒体保密通信中应用的进展[J]. 电子与信息学报, 2016, 38(3): 735-752. doi: 10.11999/JEIT151356.

YU Simin, L 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.

黄宇, 刘玉峰, 彭志敏, 等. 基于量子并行粒子群优化算法的分数阶混沌系统参数估计[J]. 物理学报, 2015, 64(3): 228-235. doi: 10.7498/aps.64.030505.

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.

黄锦旺, 冯久超, 吕善翔. 混沌信号在无线传感器网络中的盲分离[J]. 物理学报, 2014, 63(5): 50502. doi: 10.7498/aps.63. 050502.

HUANG Jinwang, FENG Jiuchao, and L Shanxiang. Blind source separation of chaotic signals in wireless sensor networks[J]. Acta Physica Sinica, 2014, 63(5): 50502. doi: 10.7498/aps.63.050502.

王世元, 冯久超. 一种新的参数估计方法及其在混沌信号盲分离中的应用[J]. 物理学报, 2012, 61(17): 170508. doi: 05.45.-a,05.45.Vx,84.40.Ua.

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.

陈越, 吕善翔, 王梦蛟, 等. 一种基于人工蜂群算法的混沌信号盲分离方法[J]. 物理学报, 2015, 64(9): 090501. doi: 10.7498 /aps.64.090501.

CHEN Yue, L 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.

OU Shifeng, WANG Xianyun, and GAO Ying. Adaptive improved RLS algorithm for blind source separation[J]. Wydawnictwo SIGMA-NOT, 2013, 89(3b): 81-83.

欧世峰, 高颖, 赵晓晖. 自适应组合型盲源分离算法及其优化方案[J]. 电子与信息学报, 2011, 33(5): 1243-1247. doi: 10.3724/SP.J.1146.2010.00871.

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.

欧世峰, 高颖, 赵晓晖. 基于随机梯度的变动量因子自适应白化算法[J]. 自动化学报, 2012, 38(8): 1370-1374. doi: 10.3724/ SP.J.1004.2012.01370.

OU Shifeng, GAO Ying, and ZHAO Xiaohui. Stochastic gradient based variable momentum factor algorithm for adaptive whitening[J]. Acta Automatica Sinica, 2012, 38(8): 1370-1374. doi: 10.3724/SP.J.1004.2012.01370.

欧世峰, 耿超, 高颖. 动量项盲源分离算法及其性能优化策略[J]. 电子学报, 2014, 42(1): 42-48. doi: 10. 3969/j.issn.0372- 2112.2014.01.007.

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.

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.

季策, 杨坤, 陶奕名, 等. 一种非平稳环境下的自适应变步长盲源分离算法[J]. 控制与决策, 2016, 31(4): 735-739. doi: 10.13195/j.kzyjc.2015.0170.

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.

CARDOSO J F and LAHELD B H. Equivariant adaptive source separation[J]. IEEE Transactions on Signal Processing, 1997, 44(12): 3017-3030.

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.

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.

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.

季策, 杨坤, 王艳茹, 等. 基于符号算子的变步长不完整自然梯度算法[J]. 模式识别与人工智能, 2014, 27(11): 1026-1031. doi: 10.16451/j.cnki.issn1003-6059.2014.11.002.

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.

徐成发, 郝宇星, 陆潞, 等. 基于互相关的快速角度估计算法[J].电子与信息学报, 2016, 38(6): 1446-1451. doi: 10.11999/ JEIT151021.

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.

Relative Articles

Supplements(0)

Cited By

Proportional views