ABSTRACT The performance of the adaptive beamformer is not only related to the array weight but also influenced by the array structure. Different sparse array configurations exhibit varying sensitivities to uncertainties in the signal direction of arrival (DOA). To address the degradation of array gain caused by desired signal DOA mismatch, this paper proposes an effective robust sparse array design method. Because DOA uncertainty leads to deviations in the steering vector (SV), we first introduce a SV uncertainty set into the sparse array design model and impose a reweighted ‐norm penalty to promote sparsity in the weight vector. The joint optimisation model is efficiently solved using the alternating direction method of multipliers (ADMM), yielding a sparse array structure with inherent resistance to DOA mismatch. Subsequently, the beamforming weight vector is optimised in two aspects. On the one hand, an improved integral reconstruction method is adopted to reconstruct the interference‐plus‐noise covariance matrix (INCM), avoiding the influence of desired signal components in the covariance matrix. On the other hand, based on subspace projection and power constraint principles, we have proposed a sparse array SV estimation algorithm to further enhance the beamforming performance. Simulation results demonstrate that the proposed approach can effectively solve the problem of serious degradation of the output signal‐to‐interference‐plus‐noise ratio (SINR) caused by DOA mismatch. It enhances beamforming robustness when prior information such as ideal INCM and accurate interference signal DOAs are unavailable.
Li et al. (Thu,) studied this question.