In asynchronous (intermittent) interference scenarios, the content of co-channel interference sources over the data interval may be different from the interferers content over the training interval, typically with extra interference sources presented over the data interval. Under such conditions, conventional adaptive beamformer designed over the training interval may lose its efficiency when applied to the data interval. In this paper, we address the problem by 1) formulating a family of the second order statistics adaptive beamformers regularized by the covariance matrix estimated over the data interval; 2) proposing a maximum likelihood methodology for optimization of the combined (mixed) covariance matrix based on maximization of a product of a likelihood ratio that checks the accuracy of the recovered training signals and a likelihood ratio on equality of the eigenvalues in complementary to the signal subspace defined over the data interval; 3) demonstrating efficiency and robustness of the proposed solution as a linear adaptive beamformer and as an initialization for iterative beamformer with projections to the finite alphabet in different asynchronous interference scenarios comparing with the basic training and data based interference rejection combining receivers.

中文翻译：

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自适应异步干扰抑制波束形成器的最大似然优化


在异步（间歇）干扰场景中，数据间隔上的同信道干扰源的内容可能与训练间隔上的干扰源内容不同，通常在数据间隔上呈现额外的干扰源。在这种情况下，在训练间隔上设计的传统自适应波束形成器在应用于数据间隔时可能会失去效率。在本文中，我们通过以下方法解决该问题：1）制定一系列二阶统计自适应波束形成器，通过数据间隔上估计的协方差矩阵进行正则化； 2) 提出一种最大似然方法，用于基于检查恢复的训练信号的准确性的似然比和与信号互补的特征值相等的似然比的乘积的最大化来优化组合（混合）协方差矩阵在数据间隔上定义的子空间； 3) 与基本训练和基于数据的干扰抑制组合接收器相比，证明所提出的解决方案作为线性自适应波束形成器和迭代波束形成器的初始化的效率和鲁棒性，在不同的异步干扰场景中投影到有限字母表。