Robust Adaptive Array Beamforming With Subspace Steering Vector Uncertainties

Abstract

In this letter, the steering vector of the signal-of-interest (SOI) is assumed to belong to a given linear subspace but the associated coordinates are otherwise unknown. In the case where the signal subspace is poorly estimated, a rank-constrained optimization problem is formulated in which the signal subspace is forced to intersect with the known linear subspace (where the SOI steering vector is located). Such problem can be reformulated as two subproblems via the variable alternation method. Also, the analytical solutions of these two subproblems can be found. Numerical results demonstrate that the proposed method can be regarded as an improved subspace-based method, which lowers the requirements in terms of snapshot number or signal-to-noise ratio (SNR) to outperform the diagonal-loading-type methods as compared with the traditional signal-subspace projection method.