Sparse Cyclic Coordinate Descent for Efficient Frequency Estimation

Yuneisy Garcia Guzman; Michael Lunglmayr

Sparse Cyclic Coordinate Descent for Efficient Frequency Estimation

Yuneisy Garcia Guzman
, Michael Lunglmayr

Institut für Signalverarbeitung

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

We present an approach for low complexity frequency estimation based on Sparse Cyclic Coordinate Descent (SCCD). We demonstrate that contrary to the Root-MUltiple Signal Classiﬁcation (Root-MUSIC) algorithm, the complexity of our proposed algorithm only scales linearly with the number of measurements as well as the number of frequencies considered on a grid. The presented algorithm is especially suited for digital hardware implementations. Simulations show that it achieves comparable estimation results than Root-MUSIC.

Originalsprache	Englisch
Titel	Proceedings of the 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019)
Verlag	IEEE
Seitenumfang	5
ISBN (Print)	978-1-7281-5549-4
Publikationsstatus	Veröffentlicht - Dez. 2019

Wissenschaftszweige

202017 Embedded Systems
202040 Übertragungstechnik
202 Elektrotechnik, Elektronik, Informationstechnik
202015 Elektronik
202022 Informationstechnik
202023 Integrierte Schaltkreise
202027 Mechatronik
202028 Mikroelektronik
202037 Signalverarbeitung
202041 Technische Informatik

JKU-Schwerpunkte

Digital Transformation

LCM - Center for Symbiotic Mechatronics - Förderphase 1
Amrhein, W. (Forscher*in), Bauer, S. (Forscher*in), Baumgartner, W. (Forscher*in), Buckwar, E. (Forscher*in), Del Re, L. (Forscher*in), Egyed, A. (Forscher*in), Ferscha, A. (Forscher*in), Gattringer, R. (Forscher*in), Gittler, P. (Forscher*in), Hochreiter, S. (Forscher*in), Huemer, M. (Forscher*in), Irschik, H. (Forscher*in), Jakoby, B. (Forscher*in), Koch, S. (Forscher*in), Küng, J. (Forscher*in), Langer, U. (Forscher*in), Miethlinger, J. (Forscher*in), Müller, A. (Forscher*in), Pirker, S. (Forscher*in), Saminger-Platz, S. (Forscher*in), Schagerl, M. (Forscher*in), Scheidl, R. (Forscher*in), Schlacher, K. (Forscher*in), Schöberl, M. (Forscher*in), Springer, A. (Forscher*in), Widmer, G. (Forscher*in), Zagar, B. (Forscher*in), Zeman, K. (Forscher*in) & Hoffelner, J. (Projektleiter*in)
01.01.2018 → 31.12.2021
Projekt: Geförderte Forschung › FFG - Österreichische Forschungsförderungsgesellschaft
Low Complexity Iterative Signal Processing Methods
Lunglmayr, M. (Projektleiter*in)
16.01.2015 → 28.02.2019
Projekt: Anderes › Projekt aus Wissenschaftsgebiet der Forschungseinheit

Dieses zitieren

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title = "Sparse Cyclic Coordinate Descent for Efficient Frequency Estimation",

abstract = "We present an approach for low complexity frequency estimation based on Sparse Cyclic Coordinate Descent (SCCD). We demonstrate that contrary to the Root-MUltiple Signal Classiﬁcation (Root-MUSIC) algorithm, the complexity of our proposed algorithm only scales linearly with the number of measurements as well as the number of frequencies considered on a grid. The presented algorithm is especially suited for digital hardware implementations. Simulations show that it achieves comparable estimation results than Root-MUSIC.",

author = "\{Garcia Guzman\}, Yuneisy and Michael Lunglmayr",

year = "2019",

month = dec,

language = "English",

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booktitle = "Proceedings of the 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019)",

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AU - Garcia Guzman, Yuneisy

AU - Lunglmayr, Michael

PY - 2019/12

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N2 - We present an approach for low complexity frequency estimation based on Sparse Cyclic Coordinate Descent (SCCD). We demonstrate that contrary to the Root-MUltiple Signal Classiﬁcation (Root-MUSIC) algorithm, the complexity of our proposed algorithm only scales linearly with the number of measurements as well as the number of frequencies considered on a grid. The presented algorithm is especially suited for digital hardware implementations. Simulations show that it achieves comparable estimation results than Root-MUSIC.

AB - We present an approach for low complexity frequency estimation based on Sparse Cyclic Coordinate Descent (SCCD). We demonstrate that contrary to the Root-MUltiple Signal Classiﬁcation (Root-MUSIC) algorithm, the complexity of our proposed algorithm only scales linearly with the number of measurements as well as the number of frequencies considered on a grid. The presented algorithm is especially suited for digital hardware implementations. Simulations show that it achieves comparable estimation results than Root-MUSIC.

M3 - Conference proceedings

SN - 978-1-7281-5549-4

BT - Proceedings of the 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019)

PB - IEEE

ER -

Sparse Cyclic Coordinate Descent for Efficient Frequency Estimation

Abstract

Wissenschaftszweige

JKU-Schwerpunkte

Projekte

LCM - Center for Symbiotic Mechatronics - Förderphase 1

Low Complexity Iterative Signal Processing Methods

Dieses zitieren