Multiple frequencies estimation from compressive phase-only data: algorithm and application

被引:2
作者
Fan, Rong [1 ]
Wan, Qun [1 ]
Chen, Hui [1 ]
You, Qing-Shan [2 ]
Wang, Hui [1 ]
机构
[1] Univ Elect Sci & Technol China, Dept Elect Engn, Chengdu 610054, Sichuan, Peoples R China
[2] Civil Aviat Flight Univ China, Sch Comp Sci, Guanghan 618307, Sichuan Provinc, Peoples R China
基金
中国国家自然科学基金;
关键词
phase-only data; frequency estimation; random subsampling; sparse recovery; compressed sensing; convex optimisation; SIGNAL RECONSTRUCTION; UNCERTAINTY PRINCIPLES; RECOVERY;
D O I
10.1080/00207217.2012.743229
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the article, the issue of multiple frequencies estimation from compressive phase-only data is addressed. A phase-only frequency receiver scheme based on compressive sensing technique is presented first. Then, we propose two reconstruction algorithms: one is convex; and the other is hybrid, which is based on robust optimisation and iterative hard threshold. With these methods, multiple frequencies estimation is accomplished by reconstructing the Fourier transform of the complex-valued time signal and finding peaks in the frequency domain. Simulation experiments illustrate its advantages both with noiseless and additive white Gaussian noise(AWGN) cases. Finally, the effects of phase quantisation errors (it would happen in a phase-to-digital converter) on the reconstruction algorithms are discussed.
引用
收藏
页码:1471 / 1482
页数:12
相关论文
共 26 条
[1]   Iterative hard thresholding for compressed sensing [J].
Blumensath, Thomas ;
Davies, Mike E. .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2009, 27 (03) :265-274
[2]   Sparse Recovery From Combined Fusion Frame Measurements [J].
Boufounos, Petros ;
Kutyniok, Gitta ;
Rauhut, Holger .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2011, 57 (06) :3864-3876
[3]   Signal recovery from random projections [J].
Candès, E ;
Romberg, J .
COMPUTATIONAL IMAGING III, 2005, 5674 :76-86
[4]   Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information [J].
Candès, EJ ;
Romberg, J ;
Tao, T .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) :489-509
[5]   Stable signal recovery from incomplete and inaccurate measurements [J].
Candes, Emmanuel J. ;
Romberg, Justin K. ;
Tao, Terence .
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 2006, 59 (08) :1207-1223
[6]   Atomic decomposition by basis pursuit [J].
Chen, SSB ;
Donoho, DL ;
Saunders, MA .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 1998, 20 (01) :33-61
[7]  
Cohen A, 2009, J AM MATH SOC, V22, P211
[8]   Iteratively Reweighted Least Squares Minimization for Sparse Recovery [J].
Daubechies, Ingrid ;
Devore, Ronald ;
Fornasier, Massimo ;
Guentuerk, C. Sinan .
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 2010, 63 (01) :1-38
[9]   Uncertainty principles and ideal atomic decomposition [J].
Donoho, DL ;
Huo, XM .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2001, 47 (07) :2845-2862
[10]  
Elad M, 2010, SPARSE AND REDUNDANT REPRESENTATIONS, P3, DOI 10.1007/978-1-4419-7011-4_1