Enhanced DOA Estimation Using Eigenvalue Reconstruction and Toeplitz Preprocessing

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.