Sparse representation based direction‐of‐arrival estimation in nonuniform noise via tail minimisation

Abstract

DOA estimation based on sparse representation in a non-uniform noise environment is proposed using a tail minimisation technique. The noise-free covariance matrix is modelled as diagonal and off-diagonal components relating to incoming signals. In order to combat coherent signals, the auto-and cross-correlation entries of the incident signal are considered separately by using the least square (LS) criterion. Subsequently, from the perspective of difference co-array a vectorised covariance matrix is obtained to emulate the received signal similar to the output of a single snapshot. The effect of non-uniform noise is significantly mitigated via a removal operation. A tail minimisation scheme that is capable of deriving sharper spectra peaks is then applied to detect the DOAs. The proposed approach has the advantages of a larger virtual array aperture beneficial to DOA estimations of uncorrelated and/or coherent signals. The technique can suppress false spectral peaks to a certain extent and is also computationally effective. Numerical simulation verifies the effectiveness of the proposed method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Coherent Signal DOA Estimation for MIMO Radar under Composite Background of Strong Interference and Non-Uniform Noise

“…To address the problem of coherent signal orientation under non-uniform noise, a generalized covariance matrix was constructed in [16] using a forward-backward spatial smoothing algorithm and the difference between the complex conjugate of the data matrix, which fully eliminated spatial non-uniform noise and achieved the estimation of coherent signals, but there was a problem of aperture loss. The authors of [17] proposed a DOA estimation based on the tail minimization method, which exploits the sparsity of the signal subspace and has the advantage of expanding the virtual array aperture. The authors of [18] used a spatial smoothing algorithm to reconstruct a full-rank signal covariance matrix, which is beneficial for the DOA estimation of uncorrelated or coherent signals, but with high complexity.…”