Enhanced DOA Estimation Using Eigenvalue Reconstruction and Toeplitz Preprocessing
ID:86 View Protection:ATTENDEE Updated Time:2024-10-28 13:45:28 Hits:528 Oral Presentation

Start Time:2024-10-25 16:30(Asia/Bangkok)

Duration:15min

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

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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
Master Student Department of Electrical Engineering; Faculty of Engineering; Chulalongkorn University

Submission Author
Shahzad Ali Department of Electrical Engineering; Faculty of Engineering; Chulalongkorn University
Sunita Khichar Chulalongkorn University
Ambar Bajpai GITAM UniversityMURTI Research Center Bangalore
Lunchakorn Wuttisittikulkij Chulalongkorn University
PISIT VANICHCHANUNT KMUTNB
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    Oct 24

    2024

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    Oct 27

    2024

  • Oct 14 2024

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