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基于压缩感知的欠定源信号恢复算法比较

王川川 曾勇虎 汪连栋

王川川, 曾勇虎, 汪连栋. 基于压缩感知的欠定源信号恢复算法比较[J]. 强激光与粒子束, 2018, 30: 053202. doi: 10.11884/HPLPB201830.170354
引用本文: 王川川, 曾勇虎, 汪连栋. 基于压缩感知的欠定源信号恢复算法比较[J]. 强激光与粒子束, 2018, 30: 053202. doi: 10.11884/HPLPB201830.170354
Wang Chuanchuan, Zeng Yonghu, Wang Liandong. Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation[J]. High Power Laser and Particle Beams, 2018, 30: 053202. doi: 10.11884/HPLPB201830.170354
Citation: Wang Chuanchuan, Zeng Yonghu, Wang Liandong. Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation[J]. High Power Laser and Particle Beams, 2018, 30: 053202. doi: 10.11884/HPLPB201830.170354

基于压缩感知的欠定源信号恢复算法比较

doi: 10.11884/HPLPB201830.170354
基金项目: 

CEMEE国家重点实验室开放课题 CEMEE2018K0303B

详细信息
    作者简介:

    王川川(1985—),男,博士,助理研究员,主要从事效能评估、盲信号处理研究; 874353112@qq.com

  • 中图分类号: TN911

Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation

  • 摘要: 构建了基于压缩感知的欠定盲源分离源信号恢复模型,比较研究了基于互补匹配追踪算法(CMP)、基于L1范数的互补匹配追踪算法(L1CMP)和基于修正牛顿的径向基函数算法(NRASR)实现欠定源信号恢复的应用效果。结果表明:源信号时域充分稀疏情况下,CMP,L1CMP和NRASR的恢复效果接近,但L1CMP算法计算复杂度最低;变换域充分稀疏情况下,CMP和L1CMP恢复效果接近,NRASR恢复效果较差;时域非充分稀疏情况下,CMP效果较差,L1CMP和NRASR效果接近。综合考虑,L1CMP算法效果最佳;在观测信号数和源数较少的情况下,算法在时域恢复信号精度会下降;稀疏表示法结合压缩感知重构能够提高源信号恢复的效果。
  • 图  1  时域充分稀疏源信号恢复结果

    Figure  1.  Source signal recovery effect when source signals are sparse in time domain

    图  2  m=6,n=8,某些时刻有多个源信号起主导作用时的源信号恢复结果

    Figure  2.  Source signal recovery effect when there are more than two signals playing the dominant role in time domain

    图  3  m=3,n=5,源信号非充分稀疏情况下源信号恢复仿真结果

    Figure  3.  Source signal recovery effect when m=3, n=5, and the source signals are incompletely sparse in time domain

    图  4  时域和小波域源信号恢复仿真结果

    Figure  4.  Simulation results of source signal recovery in time domain and wavelet domain

  • [1] Ghazdali A, Hakim A, Laghrib A, et al. A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation technique[J]. Theoretical Biology and Medical Modeling, 2015, 12(1): 25-39.
    [2] Xiao M, Xie S L, Fu Y L. A statistically sparse decomposition principle for underdetermined blind source separation[C]//Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems. 2005: 165-168.
    [3] 赵敏, 谢胜利, 肖明. 欠定和非完全稀疏的盲源恢复[J]. 华南理大学学报(自然科学版), 2010, 38(6): 19-23. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201006006.htm

    Zhao Min, Xie Shengli, Xiao Ming. Underdetermined and incompletely-sparse blind source separation. Journal of South China University of Technology (Natural Science Edition), 2010, 38(6): 19-23 https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201006006.htm
    [4] 严新. 欠定盲源分离中源信号恢复算法研究[D]. 西安: 西安电子科技大学, 2014.

    Yan Xin. Study on source signal recovery for underdetermined blind source separation. Xi'an: Xidian University, 2014
    [5] Donoho D L. Compressed sensing[J]. IEEE Trans Information Theory, 2006, 52(4): 1289-1306.
    [6] Candes E J. Compressive sampling[C]//Proceedings on the International Congress of Mathematicians. 2006: 1433-1452.
    [7] Candes E J, Romberg J, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Trans Information Theory, 2006, 52(2): 489-509.
    [8] 杨挺, 尚昆, 袁博, 等. 基于压缩感知的盲源信号分离检测方法[J]. 天津大学学报, 2016, 49(11): 1138-1143. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201611006.htm

    Yang Ting, Shang Kun, Yuan Bo, et al. Blind source separation detection method based on compressed sensing. Journal of Tianjin University, 2016, 49(11): 1138-1143 https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201611006.htm
    [9] 付卫红. 基于欠定盲分离的跳频信号分选和识别技术研究[R]. 国家自然科学基金委员会, 2015.

    Fu Weihong. Research on recovery and sorting of FH signals based on UBSS. National Natural Science Foundation of China, 2015
    [10] 付卫红, 农斌, 陈杰虎, 等. 基于RBF网络的欠定盲分离源信号恢复[J]. 北京邮电大学学报, 2017, 40(1): 94-98. https://www.cnki.com.cn/Article/CJFDTOTAL-BJYD201701017.htm

    Fu Weihong, Nong Bin, Chen Jiehu, et al. Source recovery in underdetermined blind source separation based on RBF network. Journal of Beijing University of Posts and Telecommunications, 2017, 40(1): 94-98 https://www.cnki.com.cn/Article/CJFDTOTAL-BJYD201701017.htm
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出版历程
  • 收稿日期:  2017-09-05
  • 修回日期:  2017-11-08
  • 刊出日期:  2018-05-15

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