RLS algorithm for blind source separation in non-stationary environments

机译：用于非平稳环境中盲源分离的RLS算法

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A new recursive least square (RLS) algorithm based on nonlinear principal component analysis (NPCA) is proposed to address the blind source separation (BSS) problem in non-stationary environment. Forgetting factor is introduced to improve the tracking ability in non-stationary environment. The Kalman filter is used to solve the NPCA problem since its outstanding tracking performance in non-stationary environments. Simulations using the real speech source signals are used to illustrate the performance of the new RLS algorithm in static and non-stationary environments. Results show that the new RLS algorithm has faster convergence rate and better tracking capacity compared with the stochastic gradient algorithm, and previous RLS algorithm.

机译：针对非平稳环境下的盲源分离（BSS）问题，提出了一种基于非线性主成分分析（NPCA）的递归最小二乘算法。引入遗忘因子以提高非平稳环境下的跟踪能力。卡尔曼滤波器用于解决NPCA问题，因为它在非平稳环境中具有出色的跟踪性能。使用真实语音源信号进行的仿真用于说明新RLS算法在静态和非静态环境中的性能。结果表明，与随机梯度算法和以前的RLS算法相比，新的RLS算法具有更快的收敛速度和更好的跟踪能力。

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来源
《Symposium on ICT and Energy Efficiency and Workshop on Information Theory and Security》|2012年|162-165|共4页
会议地点 Dublin(IE);Dublin(IE);Dublin(IE)
作者
Fanglin Gu; Hang Zhang; Xiaobo Tan; Desheng Zhu;
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作者单位

Institute of Communication Engineering, PLA University of Science Technology, China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
blind source separation (BSS); nonlinear principal component analysis (NPCA); recursive least square (RLS);

机译：盲源分离（BSS）;非线性主成分分析（NPCA）;递归最小二乘（RLS）;;

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专利

1. A multi-variate blind source separation algorithmA multi-variate blind source separation algorithm [J] . Goldhacker M., Keck P., Igel A., Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2017,第期

机译：多变盲源分离算法多变型盲源分离算法
2. Adaptive Improved RLS Algorithm for Blind Source Separation [J] . Shifeng OU, Xianyun WANG, Ying GAO Przeglad Elektrotechniczny . 2013,第3B期

机译：盲源分离的自适应改进RLS算法
3. Adaptive RLS algorithm for blind source separation using a natural gradient [J] . Xiao-Long Zhu, Xian-Da Zhang IEEE signal processing letters . 2002,第12期

机译：使用自然梯度的盲源分离自适应RLS算法
4. RLS algorithm for blind source separation in non-stationary environments [C] . Fanglin Gu, Hang Zhang, Xiaobo Tan, Symposium on ICT and Energy Efficiency;Workshop on Information Theory and Security . 2012

机译：非静止环境中盲源分离的RLS算法
5. New algorithms for blind equalization and blind source separation/phase recovery. [D] . Thaiupathump, Trasapong. 2002

机译：用于盲均衡和盲源分离/相位恢复的新算法。
6. Spatiospectral decomposition of multi-subject EEG: evaluating blind source separation algorithms on real and realistic simulated data [O] . David A. Bridwell, Srinivas Rachakonda, F. Silva Rogers, -1

机译：多主体脑电图的空间谱分解：基于真实和真实模拟数据评估盲源分离算法
7. INTEGRATION OF DFT AND COSINE-MODULATED FILTER BANKS WITH BLIND SEPARATION OF CONVOLUTIVELY MIXED NON-STATIONARY SOURCES [O] . Jiangtao Xi, Alfred Mertins, Joe Chicharo 2015

机译：DFT与茜茜调制滤波器的积分与盲均分混合非稳态源
8. Fast Fixed-Point Algorithms for Bayesian Blind Source Separation [R] . Lappalainen, H. 1999

机译：贝叶斯盲源分离的快速定点算法

RLS algorithm for blind source separation in non-stationary environments

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