Automotive Radar Interference Mitigation using a Convolutional Autoencoder

Jonas Fuchs, Anand Dubey, Maximilian Lübke, R. Weigel, F. Lurz
{"title":"Automotive Radar Interference Mitigation using a Convolutional Autoencoder","authors":"Jonas Fuchs, Anand Dubey, Maximilian Lübke, R. Weigel, F. Lurz","doi":"10.1109/RADAR42522.2020.9114641","DOIUrl":null,"url":null,"abstract":"Automotive radar interference imposes big challenges on signal processing algorithms as it raises the noise floor and consequently lowers the detection probability. With limited frequency bands and increasing number of sensors per car, avoidance techniques such as frequency hopping or beamforming quickly become insufficient. Detect-and-repair strategies have been studied intensively for the automotive field, to reconstruct the affected signal samples. However depending on the type of interference, reconstruction of the time domain signals is a highly non-trivial task, which can affect following signal processing modules. In this work an autoencoder based convolutional neural network is proposed to perform image based denoising. Interference mitigation is phrased as a denoising task directly on the range-Doppler spectrum. The neural networks shows significant improvement with respect to signal-to-noise-plus-interference ratio in comparison to other state-of-the-art mitigation techniques, while better preserving phase information of the spectrum compared to other techniques.","PeriodicalId":125006,"journal":{"name":"2020 IEEE International Radar Conference (RADAR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Radar Conference (RADAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR42522.2020.9114641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 33

Abstract

Automotive radar interference imposes big challenges on signal processing algorithms as it raises the noise floor and consequently lowers the detection probability. With limited frequency bands and increasing number of sensors per car, avoidance techniques such as frequency hopping or beamforming quickly become insufficient. Detect-and-repair strategies have been studied intensively for the automotive field, to reconstruct the affected signal samples. However depending on the type of interference, reconstruction of the time domain signals is a highly non-trivial task, which can affect following signal processing modules. In this work an autoencoder based convolutional neural network is proposed to perform image based denoising. Interference mitigation is phrased as a denoising task directly on the range-Doppler spectrum. The neural networks shows significant improvement with respect to signal-to-noise-plus-interference ratio in comparison to other state-of-the-art mitigation techniques, while better preserving phase information of the spectrum compared to other techniques.
基于卷积自编码器的汽车雷达干扰抑制
汽车雷达干扰对信号处理算法提出了很大的挑战,因为它提高了本底噪声,从而降低了检测概率。随着有限的频带和每辆车越来越多的传感器,跳频或波束形成等避免技术很快就会变得不够用。检测和修复策略在汽车领域得到了深入的研究,以重建受影响的信号样本。然而,根据干扰类型的不同,时域信号的重建是一项非常重要的任务,它会影响到后续的信号处理模块。本文提出了一种基于自编码器的卷积神经网络来实现基于图像的去噪。干扰抑制被描述为直接在距离-多普勒频谱上的去噪任务。与其他最先进的缓解技术相比,神经网络在信噪比和干扰比方面表现出显著的改善,同时与其他技术相比,神经网络更好地保留了频谱的相位信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信