Automatic robust adaptive beamforming via ridge regression
Automatic robust adaptive beamforming via ridge regression
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DOI：
10.1016/j.sigpro.2007.07.003
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发表时间：
2008-01-01
影响因子：
4.4
通讯作者：
Stoica, Peter
中科院分区：
文献类型：
--
作者：
Selen, Yngve;Abrahamsson, Richard;Stoica, Peter
本文利用单位增益约束最小方差问题的广义旁瓣抵消器重新参数化，导出了一类新的无参数鲁棒自适应波束形成器。在这种参数化的最小方差bearnformer获得的线性最小二乘（LS）问题的解决方案。在不准确的导向矢量和/或很少的数据快照的情况下，该略微超定的系统给出了不合适的拟合，导致标准最小方差（LS）解中的信号抵消。通过使用岭回归技术正则化LS问题，我们得到了一类鲁棒自适应bearnformers，其中没有一个需要选择用户参数，而不是许多现有的方法。在这方面，我们还提出了一个参数自由经验贝叶斯为基础的岭回归技术，据我们所知，是新颖的。我们的方法的性能示出了数值模拟和其他强大的自适应波束形成器相比。(c)2007 Elsevier B. V.保留所有权利。
In this paper we derive a class of new parameter free robust adaptive beamformers using the generalized sidelobe canceler reparameterization of the unit gain constrained minimum variance problem. In this parameterization the minimum variance bearnformer is obtained as the solution of a linear least squares (LS) problem. In the case of an inaccurate steering vector and/or few data snapshots this marginally overdetermined system gives an ill fit causing signal cancellation in the standard minimum variance (LS) solution. By regularizing the LS problem using ridge regression techniques we get a whole class of robust adaptive bearnformers, none of which requires the choice of a user parameter, as opposed to many existing methods. In this context we also propose a parameter free empirical Bayes-based ridge regression technique which, to the best of our knowledge, is novel. The performance of our approach is illustrated by numerical simulations and compared to other robust adaptive beamformers. (c) 2007 Elsevier B.V. All rights reserved.