Abstract
Nested array can expand the degrees of freedom (DOF) from difference coarray perspective, but suffering from the performance degradation of direction of arrival (DOA) estimation in unknown non-uniform noise. In this paper, a novel diagonal reloading (DR) based DOA estimation algorithm is proposed using a recently developed nested MIMO array. The elements in the main diagonal of the sample covariance matrix are eliminated; next the smallest MN-K eigenvalues of the revised matrix are obtained and averaged to estimate the sum value of the signal power. Further the estimated sum value is filled into the main diagonal of the revised matrix for estimating the signal covariance matrix. In this case, the negative effect of noise is eliminated without losing the useful information of the signal matrix. Besides, the degrees of freedom are expanded obviously, resulting in the performance improvement. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithm.
Highlights
Direction of arrival (DOA) estimation, which has been studied for decades, has been widely used in target localization, wireless communication, and so on
With the introduction of sparse recovery theory, the DOA estimation has come into a new era and algorithms such as Khatri-Rao Multiple Signal Classification (MUSIC) (KR-MUSIC) and Spatial Smoothing MUSIC (SS-MUSIC) are developed to improve the DOA estimation performance [2]
We proposed a novel diagonal reloading (DR) based DOA estimation algorithm to solve the non-uniform noise problem for sparse array
Summary
Direction of arrival (DOA) estimation, which has been studied for decades, has been widely used in target localization, wireless communication, and so on. Based on the matrix completion theory, the estimation of the noise-free covariance matrix is solved by the rank minimization [16], and nuclear norm minimization is employed to transform the non-convex problem to a convex one This method avoids the iterative optimization; it is computationally efficient. To solve the non-uniform noise problem in sparse signal recovery, He Z. et al propose a novel covariance sparsity aware (CSA) DOA estimation method in [20], which directly removes the unknown noise variances by a linear transformation. This method improves the accuracy, but it causes the loss of effective aperture.
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