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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Recursive Extended Instrumental Variable Based LCMV Beamformers for Planar Radial Coprime Arrays Under Spatially Colored Noise
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Recursive Extended Instrumental Variable Based LCMV Beamformers for Planar Radial Coprime Arrays Under Spatially Colored Noise

机译：基于扩展仪器变量的基于LCMV波束形成器，用于平面辐射的平面彩色噪声下的平面径向阵列

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摘要

This article proposes a new recursive linearly constrained minimum variance (LCMV) beamformer based on the extended instrumental variable (EIV) method for planar radial coprime arrays (PRCAs) under spatially colored noise. The proposed recursive LCMV beamformer is able to deal with multiple constraints with high precision and low complexity and can be applicable to various array geometrical configurations. Taking advantage of the EIV vector, the proposed beamformer can effectively combat the additive color noise with unknown noise covariance matrix. We develop our recursive LCMV beamformer based on the square-root (SR) EIV algorithm due to its improved numerical stability than the conventional EIV-based algorithms. Furthermore, we studied a class of planar arrays called PRCAs, which consists of a set of linear coprime arrays arranged radially at various azimuth angles. The coprime array property is utilized to enlarge the array aperture leading to higher resolution and stronger interference rejection and it offers additional flexibility in the tradeoffs between array complexity and performance. Simulation results demonstrate that the proposed recursive SREIV-based LCMV beamformer outperforms the conventional QR decomposition based LCMV beamformers in the resolution and suppression of interferences under various scenarios. The PRCAs tested outperform the uniform rectangular arrays with the same number of elements. Moreover, better performance can be achieved with more linear subarrays at the expense of increased complexity.
机译：本文提出了基于在空间彩色噪声下基于用于平面径向COPRIME阵列（PRCAS）的扩展仪器变量（EIV）方法的新递归线性约束的最小方差（LCMV）波束形成器。所提出的递归LCMV波束形成器能够处理具有高精度和低复杂度的多个约束，并且可以适用于各种阵列几何配置。利用EIV向量，所提出的波束形成器可以有效地打击具有未知噪声协方差矩阵的添加性彩色噪声。我们基于Square-Root（SR）EIV算法在其提高的数值稳定性而多于传统的基于EIV的算法的数值稳定性开发递归LCMV波束形成器。此外，我们研究了一类称为PRCAS的平面阵列，其包括一组线性基准阵列，其以各方位角径向布置。 Coprime阵列属性用于放大阵列孔径，导致较高的分辨率和更强烈的干扰抑制，并且它在阵列复杂性和性能之间的权衡提供了额外的灵活性。仿真结果表明，所提出的递归基于Sreiv的LCMV波束形成器优于传统的QR分解基于LCMV波束形成器，在各种场景下的干扰抑制。使用相同数量的元素测试PRCAS测试优于均匀的矩形阵列。此外，可以通过增加复杂性增加的线性子阵列来实现更好的性能。

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