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Intelligent Beamforming Algorithm with Low Data Quality

In the field of radar digital signal processing, adaptive beamforming technology is a very valuable algorithm. For an actual system, the array may have outliers. These outliers affect the convergence of the adaptive beamforming algorithm, resulting in poor robustness of the algorithm under outliers. Therefore, this thesis applies a Huber-loss function and AdaGrad optimization algorithm in machine learning, and proposes a robust adaptive beamforming algorithm under strong interference. Through the simulation analysis, the Huber-loss loss function is insensitive to outliers, and the AdaGrad optimization algorithm can dynamically adjust the learning rate of each gradient component, making the optimization process converge faster, and the convergence is less sensitive to scale changes. Therefore, the paper applies the both to the adaptive beamforming algorithm, minimizing the influence of outliers on the adaptive algorithm and improving the adaptability of the algorithm to different data scales. And it can make the algorithm more robust.