Delay sampling theorem: A criterion for the recovery of multitone signal

被引:17
作者
Cao, Jiahui
Yang, Zhibo [1 ]
Sun, Ruobin
Chen, Xuefeng [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China
基金
中国国家自然科学基金;
关键词
Delay sampling theorem; Minimum combined delay; Sub-Nyquist sampling; Signal recovery; Spectrum reconstruction; NYQUIST; RECONSTRUCTION; SAMPLERS;
D O I
10.1016/j.ymssp.2023.110523
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Periodic nonuniform sampling (PNS) is widely used in sub-Nyquist sampling schemes of multitone signals. However, what sampling pattern of PNS enables the signal to recover from the sub-Nyquist samples? Although this problem has been discussed in different schemes, the corresponding answers vary greatly. In this paper, we introduce the recurrent delay times to describe a PNS and provide a mathematical definition of signal recoverability from the perspective of delay. On this basis, we review the well-known Shannon-Nyquist sampling theorem and suggest a delay sampling theorem (DST) that a multitone signal bandlimited to [0, fmax] can be perfectly reconstructed from its periodic nonuniform samples when its minimum combined delay time ⠜min is not more than the Nyquist interval (1/2fmax). To explain and demonstrate the DST, subspace theory-based algorithms and active aliasing and de-aliasing algorithm (AADA) are proposed and used to recover the spectrum or waveform of multitone signals. The high consistency between practical and theoretical results evidently verifies the correctness of DST. In terms of sampling and recovery of multitone signal, DST extends the Shannon-Nyquist sampling theorem and naturally unifies several existing sampling strategies from the perspective of delay. DST provides a new guideline for the sub-Nyquist sampling of multitone signals. According to the DST criterion, we can optimize the sampling patterns to reduce cosets and improve the supremum frequency such that the reconstructed spectrum is alias-free. By generalizing the sampling theorem from the view of delay, DST opens many avenues for the development of multitone signal processing.
引用
收藏
页数:17
相关论文
共 50 条
[11]   Robust signal recovery using the prolate spherical wave functions and maximum correntropy criterion [J].
Zou, Cuiming ;
Kou, Kit Ian .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 104 :279-289
[12]   Joint Sampling and Recovery of Correlated Sources [J].
Shlezinger, Nir ;
Salamatian, Salman ;
Eldar, Yonina C. ;
Medard, Muriel .
2019 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2019, :385-389
[13]   Dualization of Signal Recovery Problems [J].
Combettes, Patrick L. ;
Dinh Dung ;
Vu, Bang Cong .
SET-VALUED AND VARIATIONAL ANALYSIS, 2010, 18 (3-4) :373-404
[14]   Papoulis? sampling theorem: Revisited [J].
Tantary, Azhar Y. ;
Shah, Firdous A. ;
Zayed, Ahmed I. .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2023, 64 :118-142
[15]   The springback penalty for robust signal recovery [J].
An, Congpei ;
Wu, Hao-Ning ;
Yuan, Xiaoming .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2022, 61 :319-346
[16]   A Sampling Theorem for Deconvolution in Two Dimensions [J].
McDonald, Joseph ;
Bernstein, Brett ;
Fernandez-Granda, Carlos .
SIAM JOURNAL ON IMAGING SCIENCES, 2020, 13 (04) :1754-1780
[17]   Classical sampling theorem in digital holography [J].
Grebenyuk, K. A. .
1ST INTERNATIONAL SCIENTIFIC SCHOOL ON METHODS OF DIGITAL IMAGE PROCESSING IN OPTICS AND PHOTONICS, 2014, 536
[18]   Generalized Sampling Theorem for Bandpass Signals [J].
Ales Prokes .
EURASIP Journal on Advances in Signal Processing, 2006
[19]   Spectrum Blind Recovery and Application in Non-uniform Sampling Based Automatic Modulation Classifier [J].
Joshi, Himani ;
Darak, Sumit J. ;
Louet, Yves .
CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2018, 37 (08) :3457-3486
[20]   Delay Coprime Sampling: A Simplified Sub-Nyquist Sampling for Noisy Multi-Sinusoidal Signals [J].
Cao, Jiahui ;
Yang, Zhibo ;
Chen, Xuefeng .
IEEE SIGNAL PROCESSING LETTERS, 2024, 31 :1720-1724