Wideband Adaptive Beamforming with Wide Nulling and Low Sidelobes Based on Sparse Array

To address the performance degradation of traditional wideband beamforming methods caused by the mismatch between array weight vectors and actual received data under fast-moving interference, this paper proposes a sparse array-based low-sidelobe null broadening algorithm. First, the frequency-domain covariance matrix is reconstructed via a focusing matrix. Subsequently, with the objective of maximizing the output signal-to-interference-plus-noise ratio (SINR), a sparsity constraint on the array weight vector is introduced to preliminarily construct an optimization model for sparse array design. Further, sidelobe constraints and derivative constraints on virtual interference steering vectors are imposed. The sparse array configuration and corresponding element weights are iteratively solved through alternating convex optimization. Simulation results demonstrate that the proposed method achieves both null broadening and deepening with fewer array elements, effectively suppressing fast-moving strong interference. Additionally, it overcomes the high sidelobe issue inherent in traditional methods and mitigates performance degradation caused by frequency-dependent beam pattern distortion in wideband signals, thereby significantly enhancing beamforming robustness.