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 ÿ 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，自适应波束形成器的性能可能在转向误差的存在下降低。在这种情况下，对角线加载是用于鲁棒波束成形的最流行的方法之一，并且可以从L 2 规范约束导出。等效地，该方法在波束形成向量上假设一个白色高斯，类似于统计观点中的脊回归。通过改变装载水平，可以将其视为对该先前分配的置信度，获得鲁棒性和适应性之间的权衡。在本文中，我们通过i p 规范概括了这种方法。我们发现，在不同的设置下，与其他PÃ,4相比，将P = 2设置为诸如先前方差的方式选择的加载级别，这不是最佳的。我们派生了迭代表单来计算波束形成器，以及迭代在线实现。在经验模拟中观察到收敛，并在某些条件下讨论。
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