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Reactance domains unitary MUSIC algorithms based on real-valued orthogonal decomposition for electronically steerable parasitic array radiator antennas

机译:电子可控寄生阵列辐射器天线基于实值正交分解的电抗域unitMUSIC算法

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摘要

This study proposes three algorithms, requiring only real-valued operations, to estimate the directions of arrival (DoAs) of correlated sources impinging onto electronically steerable parasitic array radiator (ESPAR) antennas. The constraints on the proposed algorithms are the same as those imposed onto the reactance domains-MUSIC (RD-MUSIC) algorithm allowing superior high-resolution localisation capabilities even for correlated sources scenarios with reduced computational cost as well as a low processing time compared with existing schemes. The first of the three proposed algorithms is a real-valued formulation of the standard MUSIC algorithm for ESPAR antennas that reduces significantly the computational complexity in the eigen-analysis stage. However, the other two algorithms are based on real-valued orthogonal decompositions (RVOD) techniques to estimate the noise subspace of the covariance matrices. We demonstrate that both the RVOD techniques can efficiently replace the requirement of singular value decomposition or eigenvalue decomposition which reduces further the computational complexity and makes the DoAs estimation faster. The Cramer Rao bound on the variance of DoAs estimated by the three proposed algorithms is analysed. The asymptotic performance of the developed methods is studied and compared with conventional antenna arrays. The simulation results confirm that the developed methods achieve superior precision and accuracy in DoAs estimation compared to RD-MUSIC even for correlated signals and prove the validity of our approach.
机译:这项研究提出了三种算法,仅需进行实值运算即可估计撞击到电子可控寄生阵列辐射器(ESPAR)天线上的相关源的到达方向(DoAs)。所提出算法的约束与施加于电抗域的约束相同-MUSIC(RD-MUSIC)算法,即使在相关源场景下,与现有相比,仍具有出色的高分辨率定位功能,与之相比,其计算成本降低且处理时间短计划。所提出的三种算法中的第一种是ESPAR天线的标准MUSIC算法的实值公式化,可显着降低本征分析阶段的计算复杂性。但是,其他两种算法是基于实值正交分解(RVOD)技术来估计协方差矩阵的噪声子空间的。我们证明,两种RVOD技术都可以有效地替代奇异值分解或特征值分解的要求,从而进一步降低了计算复杂度并使DoAs估计速度更快。分析了三种算法估计的DoA方差的Cramer Rao边界。研究了所开发方法的渐近性能,并将其与常规天线阵列进行了比较。仿真结果证实,与RD-MUSIC相比,即使对于相关信号,该方法在DoAs估计中也具有更高的精度和准确性,证明了该方法的有效性。

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