一种基于牛顿迭代的自适应复盲源分离算法

doi:10.3969/j.issn.0372-2112.2014.06.014

电子学报 ›› 2014, Vol. 42 ›› Issue (6): 1125-1131.DOI: 10.3969/j.issn.0372-2112.2014.06.014

一种基于牛顿迭代的自适应复盲源分离算法

王荣杰¹, 周海峰¹, 詹宜巨², 陈美谦¹

1. 集美大学轮机工程学院, 福建厦门 361021;
2. 中山大学工学院, 广东广州 510006

收稿日期:2013-03-25 修回日期:2013-11-08 出版日期:2014-06-25
- 通讯作者:
- 周海峰
- 作者简介:
- 王荣杰 男,1981年12月出生,博士,讲师,研究方向为智能信息处理.E-mail：Roger811207@163.com
- 基金资助:
- 国家自然科学基金 (No.51309116，No.51179074）; 集美大学科研基金资助项目 (No.ZQ2013001，No.ZQ2013007，No.ZC2013012，No.S13060）

An Algorithm for Adaptive Complex Blind Source Separation Based on Newton Update

WANG Rong-jie¹, ZHOU Hai-feng¹, ZHAN Yi-ju², CHEN Mei-qian¹

1. Marine Engineering Institute, Jimei University, Xiamen, Fujian 361021, China;
2. School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510006, China

Received:2013-03-25 Revised:2013-11-08 Online:2014-06-25 Published:2014-06-25

摘要/Abstract

摘要：

为了解决源数动态变化情况下的复数盲源分离的问题，首先采用一种基于交叉验证技术的源数动态估计方法；利用牛顿迭代法推导了一种自适应的复数盲源分离算法，由于在分析过程不需要对复值源信号做任何限制或约束性的假设前提，因此该算法适合于分离服从正则或非正则分布的超高斯和亚高斯信号；提出的算法通过了源数动态变化仿真实验的验证.另外，在此基础上提出了一种基于复盲源分离的波达方位动态估计法，该方法适用于在源数未知且动态变化的情况下对目标源的波达方位进行动态估计.

关键词: 复盲源分离, 交叉互验, 牛顿迭代法, 峰度, 自适应

Abstract:

To solve the problems of complex blind source separation in the condition of time-varying numbers of source,a dynamic number estimation method for complex-valued source based on cross-validation technique was proposed.In this method,an adaptive complex blind source separation algorithm based on kurtosis of complex source is derived by using a Newton update method.Due to the innovative complex blind separation algorithm without any restrictions or constraints on source signals,it can be used to separate sub-and sup-Gaussian sources with canonical or noncanonical distribution.The effectiveness of the proposed methods was validated by simulation of time-varying numbers of complex source.Based on the experimental result,an estimation method of dynamic direction of arrival (DOA) was proposed to online estimate DOA orientation under the case that the number of sources is unknown in advance and time-varying.

Key words: complex blind source separation, cross validation, Newton update algorithm, kurtosis, adaptive

中图分类号:

TN911.7

王荣杰, 周海峰, 詹宜巨, 陈美谦. 一种基于牛顿迭代的自适应复盲源分离算法[J]. 电子学报, 2014, 42(6): 1125-1131.

WANG Rong-jie, ZHOU Hai-feng, ZHAN Yi-ju, CHEN Mei-qian. An Algorithm for Adaptive Complex Blind Source Separation Based on Newton Update[J]. Acta Electronica Sinica, 2014, 42(6): 1125-1131.

参考文献

[1] BINGHAM E,HYVARINEN A.A fast fixed-point algorithm for independent component analysis of complex valued signals [J].International Journal of Neural Systems,2000,10(1):1-8.
[2] WASFY B,RAGHURAM R,THOMAS Y.Complex adaptive ICA employing the conjugate gradient technique for signal separation in time-varying flat fading channels [J].Circuits,Systems,and Signal Processing,2010,29(3):469-480.
[3] ARIE Y.Performance analysis of the strong uncorrelating transformation in blind separation of complex-valued sources [J].IEEE Transactions on Signal Processing,2012,60(1):478-483.
[4] ERIKSSON J,KOIVUNEN V.Complex-valued ICA using second order statistics [A].Barros A.Machine Learning for Signal Processing[C].New York:IEEE Press,2004.183-191.
[5] OLLILA E,KOIVUNEN V.Complex ICA using generalized uncorrelating transform [J].Signal Processing,2009,89(4):365-377.
[6] SHEN H,MARTIN K.Algebraic solutions to complex blind source separation [A].Lieven D L.Lecture Notes in Computer Science [C].Heidelberg:Springer Verlag,2012.74-81.
[7] NOVEY M,ADALI T.Complex ICA by negentropy maximization [J].IEEE Transactions on Neural Networks,2008,19(4):596-609.
[8] NOVEY M,ADALI T.On extending the complex fastICA algorithm to noncircular sources [J].IEEE Transactions on Signal Processing,2008,56(5):2148-2154.
[9] ADALI T,LI H L,NOVEY M.Complex ICA using nonlinear functions [J].IEEE Transactions on Signal Processing,2008,56(9):4536-4543.
[10] ADALI T,KIM H,CALHOUN V.Independent component analysis by complex nonlinearities [A].Jose S.ICASSP [C].New York:IEEE Press,2004.525-528.
[11] IVAN D D,SERGIO C,MARIA A S.Cyclic maximization of non-Gaussianity for blind signal extraction of complex-valued sources [J].Neurocomputing,2011,74(17):2867-2873.
[12] VAN D B.Complex gradient and Hessian [J].IEE Proceedings:Vision,Image and Signal Processing,1994,141(6):380-382.
[13] NEESER F,MASSEY J.Proper complex random processes with applications to information theory [J].IEEE Transactions on Information Theory,1993,39(4):1293-1302.
[14] SUN T Y,LIU C C,TSAI S J.Blind source separation with dynamic source number using adaptive neural algorithm [J].Expert Systems with Applications,2009,36(5):8855-8861.
[15] 张贤达.现代信号处理[M].北京:清华大学出版社,2002.126-146. ZHANG XD.Modern Signal Processing [M].Beijing:Tsinghua University Press,2002.126-146.(in Chinese)
[16] LI H L,ADALI T.A class of complex ICA algorithms based on the kurtosis cost function [J].IEEE Transactions on Neural Networks,2008,19(3):408-419.
[17] LI X L,ADALI T.Complex independent component analysis by entropy bound minimization [J].IEEE Transactions on Circuits and Systems-I:Regular Papers,2010,57(7):1417-1430.
[18] WANG R J,ZHOU H F.Application of SVM in fault diagnosis of power electronics rectifier [A].Peter L.WCICA 2008[C].New York:IEEE Press,2008.1256-1260.

一种基于牛顿迭代的自适应复盲源分离算法

An Algorithm for Adaptive Complex Blind Source Separation Based on Newton Update

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陇盛, 陶蔚, 张泽东, 陶卿. 非光滑强凸情形Adam型算法的最优收敛速率[J]. 电子学报, 2022, 50(9): 2049-2059.
[2]	孔玮, 刘云, 李辉, 崔雪红, 杨浩冉. 基于全局自适应有向图的行人轨迹预测[J]. 电子学报, 2022, 50(8): 1905-1916.
[3]	钟沛龙, 黎明, 何超, 陈昊. 基于SOM聚类和自适应算子选择的高维多目标进化算法[J]. 电子学报, 2022, 50(8): 1959-1974.
[4]	江兵兵, 何文达, 吴兴宇, 项俊浩, 洪立斌, 盛伟国. 基于自适应图学习的半监督特征选择[J]. 电子学报, 2022, 50(7): 1643-1652.
[5]	易令, 李泽平. 基于深度强化学习的码率自适应算法研究[J]. 电子学报, 2022, 50(5): 1192-1200.
[6]	李纪三, 纪彦星, 曹鼎, 刘溶, 任渊. 基于广义时间窗的旋转相控阵雷达资源调度算法[J]. 电子学报, 2022, 50(5): 1050-1057.
[7]	姜奇, 文悦, 张瑞杰, 魏福山, 马建峰. 面向智能手机的自适应触屏持续认证方案[J]. 电子学报, 2022, 50(5): 1131-1139.
[8]	杨曦, 张鑫, 郭浩远, 王楠楠, 高新波. 基于不变特征的多源遥感图像舰船目标检测算法[J]. 电子学报, 2022, 50(4): 887-899.
[9]	王延达, 陈炜通, 皮德常, 岳琳. 一种自适应记忆神经网络多跳读取与覆盖度机制结合的药物推荐模型[J]. 电子学报, 2022, 50(4): 943-953.
[10]	吴孙勇, 李东升, 薛秋条, 孙希延, 蔡如华. 基于PHD滤波的雷达起伏目标检测前跟踪算法研究[J]. 电子学报, 2022, 50(3): 691-702.
[11]	李志博, 李清宝, 兰明敬, 孙剑帆. 基于镜像选择序优化的MART算法[J]. 电子学报, 2022, 50(2): 314-325.
[12]	董明, 李敬, 索永录, 唐恩贤, 马宏伟, 陈渊, 张广明, 万翔. 基于改进果蝇优化算法正交匹配追踪的超声信号降噪方法[J]. 电子学报, 2022, 50(2): 493-501.
[13]	李双明, 关欣, 王海滨. 参数自适应的析取云模糊置信规则识别方法[J]. 电子学报, 2022, 50(2): 396-403.
[14]	王洪雁, 周贺. 改善MIMO-STAP检测性能的收发空时资源配置方法[J]. 电子学报, 2022, 50(11): 2619-2628.
[15]	高雷阜, 张梦瑶, 赵世杰. 融合簇边界移动与自适应合成的混合采样算法[J]. 电子学报, 2022, 50(10): 2517-2529.