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一种应用于均匀圆阵根MUSIC波达方向估计的多项式混沌新共形映射。

A New Conformal Map for Polynomial Chaos Applied to Direction-of-Arrival Estimation via UCA Root-MUSIC.

作者信息

Van Brandt Seppe, Verhaevert Jo, Van Hecke Tanja, Rogier Hendrik

机构信息

IDLab, Department of Information Technology, Faculty of Engineering and Architecture, Ghent University-imec, 9052 Gent, Belgium.

出版信息

Sensors (Basel). 2022 Jul 13;22(14):5229. doi: 10.3390/s22145229.

Abstract

The effects of random array deformations on Direction-of-Arrival (DOA) estimation with root-Multiple Signal Classification for uniform circular arrays (UCA root-MUSIC) are characterized by a conformally mapped generalized Polynomial Chaos (gPC) algorithm. The studied random deformations of the array are elliptical and are described by different Beta distributions. To successfully capture the erratic deviations in DOA estimates that occur at larger deformations, specifically at the edges of the distributions, a novel conformal map is introduced, based on the hyperbolic tangent function. The application of this new map is compared to regular gPC and Monte Carlo sampling as a reference. A significant increase in convergence rate is observed. The numerical experiments show that the UCA root-MUSIC algorithm is robust to the considered array deformations, since the resulting errors on the DOA estimates are limited to only 2 to 3 degrees in most cases.

摘要

通过共形映射广义多项式混沌(gPC)算法,表征了随机阵列变形对均匀圆形阵列的根多重信号分类波达方向(DOA)估计(UCA根MUSIC)的影响。所研究的阵列随机变形为椭圆形,并由不同的贝塔分布描述。为了成功捕捉在较大变形时,特别是在分布边缘出现的DOA估计中的不稳定偏差,引入了一种基于双曲正切函数的新型共形映射。将这种新映射的应用与常规gPC和作为参考的蒙特卡罗采样进行了比较。观察到收敛速度显著提高。数值实验表明,UCA根MUSIC算法对所考虑的阵列变形具有鲁棒性,因为在大多数情况下,DOA估计产生的误差仅限于2到3度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb7c/9320256/f85e7c9124f9/sensors-22-05229-g001.jpg

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