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A Gradient Ascent Approach for Multiple Frequency Estimation

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Computer Aided Systems Theory – EUROCAST 2019 (EUROCAST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12014))

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Abstract

This work investigates a new approach for frequency estimation of multiple complex sinusoids in the presence of noise. The algorithm is based on the optimization of the least squares (LS) cost function using a gradient ascent algorithm. The paper studies the performance of the proposed method and compares it to other estimation techniques such as root-multiple signal classification (root-MUSIC) and the discrete-time Fourier transform (DTFT). Simulation results show the performance gains provided by the proposed algorithm in different scenarios.

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References

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Acknowledgements

This work is supported by: the COMET-K2 “Center for Symbiotic Mechatronics” of the Linz Center of Mechatronics (LCM), funded by the Austrian federal government and the federal state of Upper Austria.

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Authors and Affiliations

  1. Johannes Kepler University Linz, Linz, Austria

    Yuneisy E. Garcia Guzman, Michael Lunglmayr & Mario Huemer

  2. Institute of Signal Processing, Linz, Austria

    Yuneisy E. Garcia Guzman, Michael Lunglmayr & Mario Huemer

Authors
  1. Yuneisy E. Garcia Guzman
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  2. Michael Lunglmayr
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  3. Mario Huemer
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Corresponding author

Correspondence to Yuneisy E. Garcia Guzman .

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Guzman, Y.E.G., Lunglmayr, M., Huemer, M. (2020). A Gradient Ascent Approach for Multiple Frequency Estimation. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-45096-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45095-3

  • Online ISBN: 978-3-030-45096-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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