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一种用于城市峡谷环境中5G车辆定位的新型时延估计算法。

A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments.

作者信息

Deng Zhongliang, Zheng Xinyu, Wang Hanhua, Fu Xiao, Yin Lu, Liu Wen

机构信息

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

出版信息

Sensors (Basel). 2020 Sep 11;20(18):5190. doi: 10.3390/s20185190.

Abstract

Vehicle positioning with 5G can effectively compensate for the lack of vehicle positioning based on GNSS (Global Navigation Satellite System) in urban canyons. However, there is also a large ranging error in the non-line of sight (NLOS) propagation of 5G. Aiming to solve this problem, we consider a new time delay estimation algorithm called non-line of sight cancellation multiple signal classification (NC-MUSIC). This algorithm uses cross-correlation to identify and cancel the NLOS signal. Then, an unsupervised multipath estimation method is used to estimate the number of multipaths and extract the noise subspace. The MUSIC spectral function can be calculated by the noise subspace. Finally, the time delay of the direct path is estimated by searching the peak of MUSIC spectral function. This paper adopts the 5G channel model developed by 3GPP TR38.901 for simulation experiments. The experiment results demonstrated that the proposed algorithm has obvious advantages in terms of NLOS propagation for urban canyon environments. It provided a high-precision time delay estimation algorithm for observed time difference of arrival (OTDOA), joint angle of arrival (AOA) ranging, and other positioning methods in the 5G vehicle positioning method, which can effectively improve the positioning accuracy of 5G vehicle positioning in urban canyon environments.

摘要

利用5G进行车辆定位可以有效弥补基于全球导航卫星系统(GNSS)在城市峡谷中车辆定位的不足。然而,5G的非视距(NLOS)传播中也存在较大的测距误差。为了解决这个问题,我们考虑一种新的时延估计算法,称为非视距消除多重信号分类(NC-MUSIC)。该算法利用互相关来识别和消除NLOS信号。然后,采用一种无监督多径估计方法来估计多径数量并提取噪声子空间。通过噪声子空间可以计算出MUSIC谱函数。最后,通过搜索MUSIC谱函数的峰值来估计直达路径的时延。本文采用3GPP TR38.901开发的5G信道模型进行仿真实验。实验结果表明,所提算法在城市峡谷环境的NLOS传播方面具有明显优势。它为5G车辆定位方法中的到达时间观测差(OTDOA)、联合到达角(AOA)测距等定位方法提供了一种高精度的时延估计算法,能够有效提高5G车辆在城市峡谷环境中的定位精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69bf/7571156/128b0024b615/sensors-20-05190-g001.jpg

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