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Enhanced DOA Estimation Using Eigenvalue Reconstruction and Toeplitz Preprocessing
ID:86 View protection:Participant Only Updated time:2024-10-28 13:45:28 Views:481 Oral Presentation

Start Time:2024-10-25 16:30

Duration:15min

Session:[RS1] Regular Session 1 [RS1-1] Mobile computing, communications, 5G and beyond

Abstract
Reliable Direction of Arrival (DOA) estimation is crucial for the performance of wireless communication systems. In this paper, we introduce a refined DOA estimation method that combines eigenvalue reconstruction of the noise subspace and Toeplitz preprocessing with the Multiple Signal Classification (MUSIC) algorithm. The proposed technique enhances the consistency of the noise subspace and improves the algorithm's resolution. Extensive simulations demonstrate that the method outperforms both the standard MUSIC and the MUSIC with Eigenvalue Reconstruction (MUSIC_ER) techniques. Notably, our approach shows enhanced performance in terms of root mean square error (RMSE) across snapshot ranges from 1 to 10. These enhancements make the proposed method (MUSIC_TR) a practical and effective option, especially in low-snapshot scenarios, providing an alternative solution for DOA estimation.
Keywords
DOA, MUSIC, Toeplitz Preprocessing, Radar, Wireless Communication
Speaker
Shahzad Ali
Department of Electrical Engineering; Faculty of Engineering; Chulalongkorn University

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Important Dates
  • Conference date

    10-24

    2024

    -

    10-27

    2024

  • 10-14 2024

    Draft paper submission deadline

  • 10-29 2024

    Registration deadline

  • 10-31 2024

    Presentation submission deadline

Sponsored By

United Societies of Science
King Mongkut's University of Technology North Bangkok (KMUTNB)
IEEE Thailand Section
IEEE Thailand Section C Chapter

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