It is well known that the performance of adaptive beamformers may degrade in the presence of steering errors. In this context, diagonal loading is one of the most popular methods used for robust beamforming, and can be derived from an l2 norm constraint. Equivalently, this method assumes a white Gaussian prior on the beamforming vector, similar to ridge regression in statistical point of view. By changing the loading level, which can be treated as confidence of this prior distribution, a trade-off between robustness and adaptivity is obtained. In this article, we generalize this approach via Ip norms. We find that under different settings, it is not optimal to set p = 2 compared with other p ÃÂÿ with the loading level chosen in such a way that the prior variance is maintained. We derive an iterative form to calculate the beamformer, as well as an iterative online implementation. Convergence is observed in empirical simulations and discussed under certain conditions.
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机译：众所周知，在存在转向误差的情况下，自适应波束形成器的性能可能会下降。在这种情况下，对角加载是用于鲁棒波束成形的最受欢迎的方法之一，并且可以从l 2 范数约束中得出。等效地，此方法在波束形成矢量上假设先于白色高斯，与统计观点上的岭回归相似。通过更改负载水平（可以将其视为此先前分布的置信度），可以在鲁棒性和适应性之间进行权衡。在本文中，我们通过I p 规范对该方法进行了概括。我们发现，在不同的设置下，与其他p相比，设置p = 2并不是最佳选择，并且以保持先验方差的方式选择加载级别。我们得出一个迭代形式来计算波束形成器，以及一个迭代的在线实现。在经验模拟中观察到了收敛性，并在某些条件下进行了讨论。
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