基于卷积神经网络的AOA/TOF联合估计

2021 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2021-01-31 DOI:10.1109/ICEIC51217.2021.9369791

Suhwan Jang, Hyunwook Lee, Chungyong Lee

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引用次数: 3

摘要

提出了一种基于一维卷积神经网络(1D CNN)的联合到达角(AOA)和飞行时间(TOF)估计方法。在网络中，接收到的包含相位信息的信号向量被组织成图像，输出数据被缩放以进行无偏学习。仿真结果表明，该方法对时隙不足具有鲁棒性，训练完成后具有较低的延迟。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Convolutional Neural Networks Based Joint AOA/TOF Estimation

This paper proposes one-dimensional convolutional neural networks (1D CNN) based approach for joint angle of arrival (AOA) and time of flight (TOF) estimation. In the network, received signal vectors which contain phase information are organized as an image and output data are scaled for unbiased learning. Simulation results show robustness against insufficient time slots and has low latency once the training is completed.

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来源期刊

2021 International Conference on Electronics, Information, and Communication (ICEIC)

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