An Improved ESPRIT Algorithm Based on Generalized Eigenvectors

Estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm does not need spectral peak searching, but the estimated variance is greater than the multiple signal classification (MUSIC) algorithm. This paper proposes a generalized eigenvector-based ESPRIT algorithm, which makes full use of generalized eigenvectors of rotation invariant relationship matrix and obtains a better performance when compared with traditional ESPRIT algorithm. The experimental results show that the proposed algorithm can achieve the similar performance of the MUSIC algorithm while keeping a low computational complexity.