Augmented Quaternion Music Method for a Uniform/Sparse Cold Array

Abstract

The quaternion multiple signal classification (Q-MUSIC) algorithm reduces the dimension of covariance matrix, which would result in performance degrading of DOA estimation. An augmented quaternion MUSIC algorithm (AQ-MUSIC) based on concentered orthogonal loop and dipole (COLD) array is presented in this paper. The proposed algorithm uses an augmented quaternion formalism to model the completely polarized signals, which allows a concise and novel way to an augmented covariance matrix. The fact reveals that more accurate DOA parameters could be extracted from an augmented covariance matrix. Even compared with the long vector MUSIC (LV-MUSIC) algorithm whose dimension of covariance matrix is the same as AQ-MUSIC, the accuracy of DOA parameter estimation is also improved. Simulation results verify the performance promotion of the proposed approach.