THE ELEVENTH IEEE SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (IEEE SAM2020)

145 / 2019-12-15 08:43:00

Underdetermined DOA Estimation based on Original Covariance Matrix using Sparse Array

Underdetermined DOA estimation; sparse array; original covariance matrix estimation(OCME); covariance matrix Toeplitz reconstruction (CMTR); sparse covariance matrix reconstruction(SCMR)

In this paper, we provide a novel insight over the DOA estimation problem for sparse array with accurate covariance matrix estimation of the equal aperture uniform linear array (ULA), called original uniform linear array (OULA). Specifically, superior performance estimation of covariance matrix of the original uniform linear array is derived from exploiting the Toeplitz structure of the covariance matrix. Meanwhile, covariance matrix estimation can be transformed as convex optimization problem and can even find more signals than DOFs of sparse array, such as coprime array. By implementing numerical simulation experiments, we demonstrate that our proposed algorithm can outperform other existing methods in terms of DOA underdetermined estimation performance, estimation accuracy, spend time and resolution ability.