Inverse Beamforming Algorithm for Strong Interference Suppression Based on Parameter-Adaptive Optimization

To improve the interference suppression performance of the inverse beamforming algorithm, an improved inverse beamforming algorithm based on the least squares criterion is proposed in this paper. Firstly, the array manifold matrix is constructed, and its generalized inverse matrix is multiplied by the beam-domain data of interference to obtain the element-domain interference data. Subsequently, the constructed element-domain interference data is subtracted from the original element-domain data to realize interference cancellation. This paper theoretically analyzes why the traditional inverse beamforming algorithm forms nulls towards interference directions yet retains prominent residual power in the regions immediately adjacent to the nulls. The proposed algorithm adopts grid search and Bayesian optimization to realize adaptive parameter selection. The theoretical analysis proves that the new algorithm can completely eliminate residual power, adjust the width of interference nulls, and further improve the local signal-to-noise ratio (SNR). Simulation results show that compared with the conventional inverse beamforming algorithm, the proposed algorithm increases the local SNR by 2–3 dB, suppresses the in-mainlobe power by no less than 30 dB, and enables adjustable width of interference nulls. Sea trial data further verifies the effectiveness and superior performance of the proposed algorithm.