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Separable linearly constrained minimum variance beamformers
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DOI：10.1016/j.sigpro.2018.12.010.png)
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Abstract
En 中文
Large-scale antenna systems offer many attractive features, including large array gain and improved spatial resolution, for example. However, classical beamforming methods, such as the linearly constrained minimum variance (LCMV) filter, do not perform well on this scenario due to large computational costs involved. To deal with this issue, we propose Kronecker-separable extensions of the LCMV filter and its stochastic gradient implementation, known as Frost's algorithm, for uniform rectangular arrays. We study the convergence of the proposed methods, investigate their computational complexity, and assess their source recovery performance with computer simulations. Our results show that our methods exhibit important computational savings while the source recovery performance losses are small. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Beamforming
LCMV
Stochastic gradient
Kronecker product
Khatri-Rao product
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