Two-Dimensional Adaptive Beamforming Based on Atomic-Norm Minimization

Adaptive beamformers usually require a great number of snapshots to update the weight vectors for large phased array antennas and the training data can easily corrupted by target signals. These challenges hinder real-time applications and significantly degrade existing methods. In this context, we propose a two-dimensional (2-D) adaptive beamforming scheme based on atomic-norm optimization. The steering matrix is first reconstructed as a vector using Kronecker product. Then, the interference covariance matrix and target direction are estimated simultaneously by formulating an atomic-norm minimizing problem. This non-convex problem is solved efficiently using alternative optimization which decomposes it into two iterative stages. The proposed beamformer is free from the influence of target signals and able to adjust pointing direction adaptively. The superiority of the proposed method over other competitive methods are verified using numerical simulations.