Abstract: Adaptive beamformers are sensitive to model mismatch and moving interference, especially when the desired signal is present in training snapshots, even a small error can lead to a serious degradation in performance. A robust adaptive beamforming algorithm with null widening is proposed, which is performed by reconstructing and optimizing the interference-plus-noise covariance matrix, and estimating the true steering vector. The sidelobe can be optimized by adjusting the beam width of the mainlobe. Simulation results demonstrate the correctness and effectiveness of the proposed algorithm.