Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

doi:10.19682/j.cnki.1005-8885.2019.1030

中国邮电高校学报(英文) ›› 2019, Vol. 26 ›› Issue (6): 94-102.doi: 10.19682/j.cnki.1005-8885.2019.1030

• Signal processing • 上一篇

Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

Wang Chuanchuan, Zeng Yonghu, Wang Liandong, Fu Weihong

1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China
2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China

收稿日期:2019-09-02 修回日期:2019-12-22 出版日期:2019-12-31 发布日期:2020-03-10
通讯作者: Wang Chuanchuan, E-mail: wangchuan1083@126.com E-mail:wangchuan1083@126.com
作者简介:Wang Chuanchuan, E-mail: wangchuan1083@126.com

Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

Wang Chuanchuan, Zeng Yonghu, Wang Liandong, Fu Weihong

1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China
2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China

Received:2019-09-02 Revised:2019-12-22 Online:2019-12-31 Published:2020-03-10
Contact: Wang Chuanchuan, E-mail: wangchuan1083@126.com E-mail:wangchuan1083@126.com
About author:Wang Chuanchuan, E-mail: wangchuan1083@126.com

摘要/Abstract

摘要： Aiming at the statistical sparse decomposition principle (SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP (ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix's column vectors used for signal recovery is modified, which enables the choose of mixing matrix's column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery.

关键词:

underdetermined blind source separation, signal recovery, ISSDP

Abstract: Aiming at the statistical sparse decomposition principle (SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP (ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix's column vectors used for signal recovery is modified, which enables the choose of mixing matrix's column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery.

Key words:

underdetermined blind source separation, signal recovery, ISSDP

中图分类号:

TN911

Wang Chuanchuan, Zeng Yonghu, Wang Liandong, Fu Weihong.

Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

[J]. The Journal of China Universities of Posts and Telecommunications, 2019, 26(6): 94-102.

参考文献

References
1. Su Q, Shen Y H, Wei Y M, et al. Underdetermined blind source separation by a novel time-frequency method. International Journal of Electronics and Communications, 2017, 77(4): 43 -49
2. Wang C C, Zeng Y H. Research status and prospects of underdetermined blind source separation algorithm. Journal of Beijing University of Posts and Telecommunications, 2018,
41(6): 113 -119
3. Wang C C, Zeng Y H, Fu W H, et al. Estimation method for an underdetermined mixing matrix based on maximum density point searching. Journal of Xidian University, 2019, 46(1): 106 -111
4. Yilmaz O, Rickard S. Blind separation of speech mixtures via time-frequency masking. IEEE Transactions on Signal Processing, 2004, 52(7): 1830 -1847
5. Cobos M, Lopez J J. Maximum a posteriori binary mask estimation for underdetermined source separation using smoothed posteriors. IEEE Transactions on Audio, Speech and Language Processing, 2012, 20(7): 2059 -2064
6. Araki S, Sawada H, Mukai R. Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors. Signal Processing, 2007, 87(2): 1833 -1847
7. Peng D Z, Xiang Y. Underdetermined blind source separation based on relaxed sparsity condition of sources. IEEE Transactions on Signal Processing, 2009, 57(2): 809 -814
8. Xiao M, Xie S L, Fu Y L. A statistically sparse decomposition principle for underdetermined blind source separation. International Symposium on Intelligent Signal Processing and Communication Systems, 2005(12): 165 -168
9. Zhao M. Research on the theory and key problems of blind source separation. Guangzhou: South China University of Technology, 2010
10. Fu W H, Chen J H, Yang B. Source recovery of underdetermined blind source separation based on SCMP algorithm. IET Signal Processing, 2017, 11(7): 877 -883
11. Fu W H, Wei J, Liu N A, et al. Algorithm for source recovery in underdetermined blind source separation based on plane pursuit. Journal of Systems Engineering and Electronics, 2018, 29(2): 223 -228
12. Fu W H, Nong B, Zhou X B, et al. Source recovery in underdetermined blind source separation based on artificial neural network. China Communications, 2018(1): 140 -145

[1]	束丰张玲华丁寅. Iterative subspace matching pursuit for joint sparse recovery[J]. 中国邮电高校学报(英文版), 2023, 30(2): 26-35.
[2]	Wang Chuanchuan, Zeng Yonghu, Wang Liandong, Li Zhipeng. Source enumeration method by combining diagonal loading and constructing second-order statistics[J]. 中国邮电高校学报(英文版), 2023, 30(1): 47-55.
[3]	Guan Sihai, Cheng Qing, Zhao Yong, Liu Fangyao. Variable step-size adaptive filtering algorithm based on an exponent sin function[J]. 中国邮电高校学报(英文版), 2023, 30(1): 56-65.
[4]	Jiang Fan, Chen Jiajun, Gao Youjun, Sun Changyin. Research on ECG classification based on transfer learning [J]. 中国邮电高校学报(英文版), 2022, 29(6): 83-96.
[5]	张承畅徐余杨建鹏李晓梦. Automatic modulation classification based on AlexNet with data augmentation[J]. 中国邮电高校学报(英文版), 2022, 29(5): 51-61.
[6]	Yao Shengshi, Wang Sixian, Dai Jincheng, Niu Kai, Xu Wenjun, Zhang Ping. Semantic information processing in industrial networks[J]. 中国邮电高校学报(英文版), 2022, 29(1): 41-49.
[7]	Li Hui, Li Shanshan, Zou Borong, Chen Yannan. Modulation classification based on the collaboration of dual-channel CNN-LSTM and residual network[J]. 中国邮电高校学报(英文版), 2022, 29(1): 113-124.
[8]	Li Xiaohui, Liu Shuaishuai, Fan Tao, Fang Kun. Dual-CRC polar codes and dual-SCFlip decoding algorithm for cell search in 5G system [J]. 中国邮电高校学报(英文版), 2021, 28(6): 82-90.
[9]	Ma Shexiang, Mei Xiaobing. Side-lobe constrained beamforming under virtual expansion of L-shaped array[J]. 中国邮电高校学报(英文版), 2021, 28(2): 79-88.
[10]	王川川姜林曾勇虎汪连栋. Underdetermined mixing matrix estimation by comprehensive application of cluster validity indexes [J]. 中国邮电高校学报(英文版), 2020, 27(6): 73-86.
[11]	周游段瑞枫蒋伯峰. Reduced-state noncoherent MLSD method for CPM in aeronautical telemetry [J]. 中国邮电高校学报(英文版), 2020, 27(4): 43-53.
[12]	Gao Miao, Yu Zhiguo, Li Qingqing, Wei Jinghe, Gu Xiaofeng. Improved ECG signal compression method for E-healthcare[J]. 中国邮电高校学报(英文版), 2019, 26(4): 62-69.
[13]	张凯尹长川 Xu Guixian 刘丹谱. Robust hybrid beamforming design for millimeter-wave massive MIMO systems[J]. 中国邮电高校学报(英文版), 2019, 26(3): 44-49.
[14]	Yang Jinsheng, Xiang Yang, Chen Weigang, Dong Yangyang. Computationally efficient 2-D DOA estimation for non-uniform two-L-shaped array[J]. 中国邮电高校学报(英文版), 2018, 25(6): 81-89.
[15]	Yuan Fang, Tian Bin. Modified min-sum SCAN decoding algorithm for polar codes[J]. 中国邮电高校学报(英文版), 2018, 25(6): 90-96.

Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

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

本文评价