Robust DOA Estimation Using Independent Component Analysis

The paper deals with robust direction of arrival (DOA) estimation based on independent component analysis (ICA) in the presence of sensor gain perturbation and unknown noise fields. It has been shown that the noise subspace based estimator suffers from the drawback of being very sensitivity to orthogonality between the estimated noise subspace and true signal subspace. However, no single subspace feature extraction gives satisfactory results under different environments, but the proposed two-step subspace feature extraction method can efficiently cope with the limits. The noise-subspace demixing procedure of complex-valued FastICA is presented here, which is based on minor component analysis (MCA) for individual noise-eigenvector projection, and Newton’s iteration algorithm exploiting the minimization of the approximate negentropy of non-Gaussian signal for array signal processing. When a new noise subspace is attained, the proposed method can form the maximizing orthogonality especially for imperfect antenna array and make to facilitate the conventional noise-subspace based estimate method. Several computer simulation examples confirm the effectiveness of the proposed method and the performance improvement over the MUSIC estimator.