Underdetermined Blind Source Separation of FSK Signal Based on Particle Swarm Optimization

被引：0

作者：

Xia Jiang-hua ^{[1
]}

Yang Li ^{[1
]}

机构：

[1] Sichuan Aerosp Vocat Coll, Chengdu 610100, Sichuan, Peoples R China

来源：

PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON ADVANCED DESIGN AND MANUFACTURING ENGINEERING (ICADME 2017) | 2017年 / 136卷

关键词：

FSK signal; Independent component analysis; Particle swarm optimization; blind signal separation; REPRESENTATIONS;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

It's likely undetermined case that the observed signal is less than the number of source number, while the FSK signal of track circuit is mixed with a variety of interference signal. The traditional independent component analysis (ICA) cannot separate source signals. The article separate source signals use the algorithm of FSK signal underdetermined blind separation based on particle swarm optimization. Finally through the computer simulation, it show this method can effectively separate the FSK signal interference signals, and have good separation performance.

引用

页码：209 / 213

页数：5

共 8 条

[1] Underdetermined blind source separation using sparse representations [J].

Bofill, P ;

Zibulevsky, M .

SIGNAL PROCESSING, 2001, 81 (11) :2353-2362

[2]

Cai Xiaodong, 2009, CHINA RAILWAY SCI, V30

[3]

[邓迎宏 Deng Yinghong], 2002, [中国铁道科学, China Railway Science], V23, P55

[4]

[郜洪民 Gao Hongmin], 2002, [中国铁道科学, China Railway Science], V23, P101

[5] Blind source separation of more sources than mixtures using overcomplete representations [J].

Lee, TW ;

Lewicki, MS ;

Girolami, M ;

Sejnowski, TJ .

IEEE SIGNAL PROCESSING LETTERS, 1999, 6 (04) :87-90

[6]

Liu Ning, 2010, CHINA RAILWAY SCI, V31

[7]

Yang Hang, 2003, ARTIFICIAL NEURAL NE

[8] Blind source separation by sparse decomposition in a signal dictionary [J].

Zibulevsky, M ;

Pearlmutter, BA .

NEURAL COMPUTATION, 2001, 13 (04) :863-882

← 1 →