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Beamspace direction finding based on the conjugate gradient and the auxiliary vector filtering algorithms

机译：基于共轭梯度和辅助矢量滤波算法的波束空间方向寻找

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摘要

Motivated by the performance of the direction finding algorithms based on the auxiliary vector filtering (AVF) method and the conjugate gradient (CG) method as well as the advantages of operating in beamspace (BS), we develop two novel direction finding algorithms for uniform linear arrays (ULAs) in the beamspace domain, which we refer to as the BS AVF and the BS CG methods. The recently proposed Krylov subspace-based CG and AVF algorithms for the direction of arrival (DOA) estimation utilize a non-eigenvector basis to generate the signal subspace and yield a superior resolution performance for closely spaced sources under severe conditions. However, their computational complexity is similar to the eigenvector-based methods. In order to save computational resources, we perform a dimension reduction through the linear transformation into the beamspace domain, which additionally leads to significant improvements in terms of the resolution capability and the estimation accuracy. A comprehensive complexity analysis and simulation results demonstrate the excellent performance of the proposed algorithms and show their computational requirements. As examples, we investigate the efficacy of the developed methods for the discrete Fourier transform (DFT) and the discrete prolate spheroidal sequences (DPSS) beam-space designs.
机译：基于辅助矢量滤波（AVF）和共轭梯度（CG）方法的测向算法的性能以及在波束空间（BS）中工作的优势，我们开发了两种新颖的均匀线性测向算法波束空间域中的阵列（ULA），我们称为BS AVF和BS CG方法。最近提出的用于到达方向（DOA）估计的基于Krylov子空间的CG和AVF算法利用非特征向量的基础来生成信号子空间，并在严酷条件下为间隔很小的信号源提供出色的分辨率性能。但是，它们的计算复杂度类似于基于特征向量的方法。为了节省计算资源，我们通过线性变换到波束空间域来执行降维，这在分辨率能力和估计精度方面也带来了显着改善。全面的复杂性分析和仿真结果证明了所提出算法的出色性能，并显示了其计算要求。作为示例，我们调查了离散傅里叶变换（DFT）和离散扁球体序列（DPSS）束空间设计的开发方法的功效。

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