A Computationally Efficient MUSIC Algorithm with an Enhanced DOA Estimation Performance for a Crossed-Dipole Array

In this article, an improved real-valued dimension-reduction MUSIC (IRDR-MUSIC) algorithm is proposed for a crossed-dipole array. Initially, conjugate symmetry of the spatial component in the manifold vector is derived such that two real-valued matrices for the sum and difference covariance are constructed, which consist of the real and imaginary parts of the complex covariance matrix respectively. However, sum and difference covariance matrices with information loss would deteriorate the performance. Thus, given that the sum and difference covariance matrices have an identical null space, a joint sum-difference covariance matrix combining both the sum and difference covariance matrices is constructed, which includes the complete information of a complex covariance matrix. Accordingly, a computationally efficient IRDR-MUSIC algorithm with an enhanced performance is proposed. Compared with the existing dimension-reduction MUSIC algorithm, the proposed IRDR-MUSIC algorithm greatly reduce the complexity reduction almost without any performance loss since singular-value decomposition of the joint sum-difference covariance matrix operates in the real-valued domain, and only half of the range of the spatial spectrum search is required. Furthermore, the proposed IRDR-MUSIC algorithm outperforms the state-of-art complex-valued, symmetry-compressed, dimension-reduction MUSIC algorithm in both its multi-target resolution and computational efficiency. Numerical simulations and analyses verify the superiority of the proposed IRDR-MUSIC algorithm.