基于  l 1 稀疏正则化的信源个数估计新算法

doi:10.11959/j.issn.1000-436x.2016198

Journal on Communications ›› 2016, Vol. 37 ›› Issue (10): 75-80.doi: 10.11959/j.issn.1000-436x.2016198

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New source number estimation algorithm based on $l_{1}$ sparse regularization

Fang-xiao JIN¹,Tian-shuang QIU¹,Peng WANG¹,Nan XIA²,Jing-chun LI²

¹ Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian 116024,China
² State Radio Monitoring Center,Beijing 100037,China

Online:2016-10-25 Published:2016-10-25
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Key Technology R＆D Program

Abstract

Abstract:

In view of the problems of inefficient in low SNR and less snapshots when using existing sources number estimation related algorithms,a new algorithm based on e₁sparse regularization under space stationary noise was proposed to estimate the number of signal sources.The algorithm estimated the sources number by using the sparse representation of eigenvalues vectors with the suitable regularization parameter.Theoretical analysis and simulation results show that the algorithm can realize an accurate sources number estimation in low SNR and less snapshots.

Key words: sparse regularization, sources number estimation, space stationary noise, regularization parameter

Fang-xiao JIN,Tian-shuang QIU,Peng WANG,Nan XIA,Jing-chun LI. New source number estimation algorithm based on $l_{1}$ sparse regularization[J]. Journal on Communications, 2016, 37(10): 75-80.

Figures/Tables 5

References 15

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u _i	SNR = ?10 dB	SNR = ?5 dB	SNR = 0 dB	SNR = 5 dB	SNR = 10 dB
u ₁	11.38	10.62	10.66	10.57	10.57
u ₂	6.04	5.91	5.83	5.80	5.77
u ₃	0.63	0.25	0.08	0.08	0.08
u ₄	0.06	5.28×10^{? 9}	7.42×10^{? 10}	2.95×10^{? 10}	2.12×10^{? 10}
u ₅	2.36×10^{? 9}	2.93×10^{? 9}	2.97 ×10^{? 10}	1.32×10^{? 10}	1.04×10^{? 10}
u ₆	2.24×10^{? 29}	5.86×10^{? 29}	5.00×10^{? 31}	5.51×10^{? 31}	6.53×10^{? 31}

正则化参数γ			估计准确率
正则化参数γ	SNR = ?20 dB	SNR = ?10 dB	SNR = 0 dB	SNR = 10 dB	SNR = 20 dB
0.5	66%	52%	0	0	0
0.55	46%	72.5%	0	0	0
0.6	0	30	100%	100%	100%

New source number estimation algorithm based on $l_{1}$ sparse regularization

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