强激光与粒子束, 2018, 30 (5): 053202, 网络出版: 2018-05-04  

基于压缩感知的欠定源信号恢复算法比较

Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation
作者单位
电子信息系统复杂电磁环境效应国家重点实验室, 河南 洛阳 471003
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。
Abstract
The source signal recovery model for underdetermined blind source separation based on compressed sensing(CS) is constructed, and the recovery effect of three algorithms separately based on the complementary matching pursuit(CMP), the L1 based complementary matching pursuit(L1CMP) and modified Newton radial basis function(NRASR) are compared by simulation. Results show that as to the completely sparse source signals in time domain, the recovery effect of the three algorithms are similar, while the calculation complexity of L1CMP is the lowest. As to the completely sparse source signals in transformation domain, the recovery effects of CMP and L1CMP are similar, but that of NRASR is worse. When the source signals are incompletely sparse in time domain, the recovery effect of CMP is worse, and those of L1CMP and NRASR are similar. So based on comprehensive consideration, the L1CMP algorithm is the best in the three algorithms. As to the case of the source signal number and observation signal number are small, the recovery effect would decline in time domain. The sparse representation method combined with the CS reconstruction algorithms can get good source signal recovery effect.
参考文献

[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.(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)

[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.(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)

[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.(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)

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(5): 053202. 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(5): 053202.

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