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

Institute of Signal Processing

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

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.

Original language	English
Title of host publication	Proceedings of the 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019)
Publisher	IEEE
Number of pages	5
ISBN (Print)	978-1-7281-5549-4
Publication status	Published - Dec 2019

Fields of science

202017 Embedded systems
202040 Transmission technology
202 Electrical Engineering, Electronics, Information Engineering
202015 Electronics
202022 Information technology
202023 Integrated circuits
202027 Mechatronics
202028 Microelectronics
202037 Signal processing
202041 Computer engineering

JKU Focus areas

Digital Transformation

LCM - Center for Symbiotic Mechatronics - Förderphase 1
Amrhein, W. (Researcher), Bauer, S. (Researcher), Baumgartner, W. (Researcher), Buckwar, E. (Researcher), Del Re, L. (Researcher), Egyed, A. (Researcher), Ferscha, A. (Researcher), Gattringer, R. (Researcher), Gittler, P. (Researcher), Hochreiter, S. (Researcher), Huemer, M. (Researcher), Irschik, H. (Researcher), Jakoby, B. (Researcher), Koch, S. (Researcher), Küng, J. (Researcher), Langer, U. (Researcher), Miethlinger, J. (Researcher), Müller, A. (Researcher), Pirker, S. (Researcher), Saminger-Platz, S. (Researcher), Schagerl, M. (Researcher), Scheidl, R. (Researcher), Schlacher, K. (Researcher), Schöberl, M. (Researcher), Springer, A. (Researcher), Widmer, G. (Researcher), Zagar, B. (Researcher), Zeman, K. (Researcher) & Hoffelner, J. (PI)
01.01.2018 → 31.12.2021
Project: Funded research › FFG - Austrian Research Promotion Agency
Low Complexity Iterative Signal Processing Methods
Lunglmayr, M. (PI)
16.01.2015 → 28.02.2019
Project: Other › Project from scientific scope of research unit

Cite this

@inproceedings{5636aaf9e9bf4f62bf082337502b582e,

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",

isbn = "978-1-7281-5549-4",

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

publisher = "IEEE",

}

TY - GEN

T1 - Sparse Cyclic Coordinate Descent for Efficient Frequency Estimation

AU - Garcia Guzman, Yuneisy

AU - Lunglmayr, Michael

PY - 2019/12

Y1 - 2019/12

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

Fields of science

JKU Focus areas

Projects

LCM - Center for Symbiotic Mechatronics - Förderphase 1

Low Complexity Iterative Signal Processing Methods

Cite this