Covariance-Based Time-Frequency ESPRIT Algorithm for Direction-of-Arrival Estimation
Seungnyun Kim, Jiao Wu, Ahnho Lee, Yiying Liu, Yongseok Byun, Byonghyo Shim · 2022 27th Asia Pacific Conference on Communications (APCC) · 2022
In this paper, a new version of time-frequency (T-F) ESPRIT algorithm with reduced computational complexity is proposed. The key idea of proposed covariance-based T-F ESPRIT (CB T-F ESPRIT) algorithm is to use the covariance-based DoA (CB-DoA) approach for the signal subspace construction. Specifically, the proposed CB T-F ESPRIT algorithm first constructs the time-frequency data model and then exploits the STFD matrix for the estimation of signal subspace. In particular, instead of directly performing EVD on the covariance matrix obtained from the averaged STFD matrix, the proposed scheme employs the CB-DoA approach which provides a lower computational complexity while maintaining the performance gain of T-F ESPRIT algorithm over the conventional ESPRIT algorithm. From the computational complexity analysis and the numerical evaluations, we demonstrate that CB T-F ESPRIT algorithm outperforms the conventional DoA estimation schemes with reduced computational complexity.