A Robust Machine Learning Based UWB AOA Estimation Method

被引：0

作者：

Zeng, Wenmin ^{[1
]}

Zhang, Jialin ^{[1
]}

Zhang, Tingting ^{[1
,2
]}

机构：

[1] Harbin Inst Technol, Sch Elect & Informat Engn, Shenzhen, Peoples R China

[2] Peng Cheng Lab, Shenzhen, Peoples R China

来源：

2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING | 2024年

关键词：

Angle of arrival (AOA); ultra-wideband (UWB); hardware imperfections; Support Vector Regression (SVR);

D O I：

10.1109/VTC2024-SPRING62846.2024.10683435

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In current main-stream ultra wideban (UWB) devices, hardware imperfections such as antenna mutual coupling, cross-polarization and signal distorations may prevent from achiving high accuracy angle of arrival (AOA) estimation. Due to the difficulties in hardware imperfection modeling, we present a non-parametric support vector regression (SVR) based robust AOA solution in this paper. A set of high relevant features extracted from received signals are adopted as the input for the rat swarm optimizer (RSO) - SVR method. Comprehensive experimental measurements are carried out, which show its performance advantages, particular in cases where traditional phase difference of arrival (PDOA) does not work well.

引用

页数：5

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