A super-resolution algorithm for multiband signal identification

Zhihui Zhu; Dehui Yang; Michael B. Wakin; Gongguo Tang

doi:10.1109/ACSSC.2017.8335193

A super-resolution algorithm for multiband signal identification

Zhihui Zhu, Dehui Yang, Michael B. Wakin, Gongguo Tang

Colorado School of Mines

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

Recent advances in convex optimization have led to super-resolution algorithms that provide exact frequency localization in multitone signals from limited time-domain samples. Such localization is accomplished by minimizing a certain atomic norm, which can be implemented in a semidefinite program. In this work, we consider the identification of multiband signals, which are comprised of multiple, unknown narrow bands of frequency content at multiple carrier frequencies. Integrating a basis of modulated discrete prolate spheroidal sequences (DPSS's) into the atomic norm minimization framework, we introduce a technique for estimating the unknown band positions based on limited time-domain samples of the signal.

Original language	English
Title of host publication	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	323-327
Number of pages	5
ISBN (Electronic)	9781538618233
DOIs	https://doi.org/10.1109/ACSSC.2017.8335193
Publication status	Published - 2 Jul 2017
Externally published	Yes
Event	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States Duration: 29 Oct 2017 → 1 Nov 2017

Publication series

Name	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume	2017-October

Conference

Conference	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Country/Territory	United States
City	Pacific Grove
Period	29/10/17 → 1/11/17

Keywords

Atomic norm
discrete prolate spheroidal sequences
multiband identification
super-resolution

Access to Document

10.1109/ACSSC.2017.8335193

Cite this

Zhu, Z., Yang, D., Wakin, M. B., & Tang, G. (2017). A super-resolution algorithm for multiband signal identification. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (pp. 323-327). Article 8335193 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335193

Zhu, Zhihui ; Yang, Dehui ; Wakin, Michael B. et al. / A super-resolution algorithm for multiband signal identification. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. editor / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 323-327 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017).

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abstract = "Recent advances in convex optimization have led to super-resolution algorithms that provide exact frequency localization in multitone signals from limited time-domain samples. Such localization is accomplished by minimizing a certain atomic norm, which can be implemented in a semidefinite program. In this work, we consider the identification of multiband signals, which are comprised of multiple, unknown narrow bands of frequency content at multiple carrier frequencies. Integrating a basis of modulated discrete prolate spheroidal sequences (DPSS's) into the atomic norm minimization framework, we introduce a technique for estimating the unknown band positions based on limited time-domain samples of the signal.",

keywords = "Atomic norm, discrete prolate spheroidal sequences, multiband identification, super-resolution",

author = "Zhihui Zhu and Dehui Yang and Wakin, {Michael B.} and Gongguo Tang",

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doi = "10.1109/ACSSC.2017.8335193",

language = "English",

series = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",

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Zhu, Z, Yang, D, Wakin, MB & Tang, G 2017, A super-resolution algorithm for multiband signal identification. in MB Matthews (ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017., 8335193, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 323-327, 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Pacific Grove, United States, 29/10/17. https://doi.org/10.1109/ACSSC.2017.8335193

A super-resolution algorithm for multiband signal identification. / Zhu, Zhihui; Yang, Dehui; Wakin, Michael B. et al.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. p. 323-327 8335193 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AB - Recent advances in convex optimization have led to super-resolution algorithms that provide exact frequency localization in multitone signals from limited time-domain samples. Such localization is accomplished by minimizing a certain atomic norm, which can be implemented in a semidefinite program. In this work, we consider the identification of multiband signals, which are comprised of multiple, unknown narrow bands of frequency content at multiple carrier frequencies. Integrating a basis of modulated discrete prolate spheroidal sequences (DPSS's) into the atomic norm minimization framework, we introduce a technique for estimating the unknown band positions based on limited time-domain samples of the signal.

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Zhu Z, Yang D, Wakin MB, Tang G. A super-resolution algorithm for multiband signal identification. In Matthews MB, editor, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 323-327. 8335193. (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017). doi: 10.1109/ACSSC.2017.8335193

A super-resolution algorithm for multiband signal identification

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