{"title":"汽车啁啾序列雷达系统宽带干扰抑制学习模型的比较","authors":"Yudai Suzuki;Xiaoyan Wang;Masahiro Umehira","doi":"10.23919/comex.2024COL0013","DOIUrl":null,"url":null,"abstract":"CS (Chirp Sequence) radar plays a crucial role in the safety of autonomous driving. However, its widespread adoption increases the probability of wideband inter-radar interference, leading to miss-detection of targets. To address this problem, we utilize RNN (Recurrent Neural Network) and self-attention models to mitigate wideband interference in automotive radar systems and compare 12 different learning models in terms of SNR (Signal-to-Noise Ratio) and processing time.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"13 12","pages":"470-474"},"PeriodicalIF":0.3000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633224","citationCount":"0","resultStr":"{\"title\":\"Comparison of Learning Models for Wideband Interference Mitigation in Automotive Chirp Sequence Radar Systems\",\"authors\":\"Yudai Suzuki;Xiaoyan Wang;Masahiro Umehira\",\"doi\":\"10.23919/comex.2024COL0013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"CS (Chirp Sequence) radar plays a crucial role in the safety of autonomous driving. However, its widespread adoption increases the probability of wideband inter-radar interference, leading to miss-detection of targets. To address this problem, we utilize RNN (Recurrent Neural Network) and self-attention models to mitigate wideband interference in automotive radar systems and compare 12 different learning models in terms of SNR (Signal-to-Noise Ratio) and processing time.\",\"PeriodicalId\":54101,\"journal\":{\"name\":\"IEICE Communications Express\",\"volume\":\"13 12\",\"pages\":\"470-474\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633224\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEICE Communications Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10633224/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Communications Express","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10633224/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Comparison of Learning Models for Wideband Interference Mitigation in Automotive Chirp Sequence Radar Systems
CS (Chirp Sequence) radar plays a crucial role in the safety of autonomous driving. However, its widespread adoption increases the probability of wideband inter-radar interference, leading to miss-detection of targets. To address this problem, we utilize RNN (Recurrent Neural Network) and self-attention models to mitigate wideband interference in automotive radar systems and compare 12 different learning models in terms of SNR (Signal-to-Noise Ratio) and processing time.
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