{"title":"一种基于加权L1范数的过壁雷达成像算法","authors":"Luo Mingshi, Z. Mengmeng","doi":"10.1109/ICSP51882.2021.9408799","DOIUrl":null,"url":null,"abstract":"This article is mainly studied based on weighted L1 norm through-wall radar imaging algorithm. Due to the interference of the environment or the radar platform, the echo data acquired by the TWR system will be mixed with some noise, which seriously affects the imaging results. In this article, the weighted L1 norm constraint model is closer to the L0 norm constraint model through imaging comparison of the four algorithms in the case of no noise and -2dB Gaussian white noise. In other words, the quality and stability of the imaging are improved by improving the weighting function.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A wall-passing radar imaging algorithm based on weighted L1 norm\",\"authors\":\"Luo Mingshi, Z. Mengmeng\",\"doi\":\"10.1109/ICSP51882.2021.9408799\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article is mainly studied based on weighted L1 norm through-wall radar imaging algorithm. Due to the interference of the environment or the radar platform, the echo data acquired by the TWR system will be mixed with some noise, which seriously affects the imaging results. In this article, the weighted L1 norm constraint model is closer to the L0 norm constraint model through imaging comparison of the four algorithms in the case of no noise and -2dB Gaussian white noise. In other words, the quality and stability of the imaging are improved by improving the weighting function.\",\"PeriodicalId\":117159,\"journal\":{\"name\":\"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSP51882.2021.9408799\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSP51882.2021.9408799","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A wall-passing radar imaging algorithm based on weighted L1 norm
This article is mainly studied based on weighted L1 norm through-wall radar imaging algorithm. Due to the interference of the environment or the radar platform, the echo data acquired by the TWR system will be mixed with some noise, which seriously affects the imaging results. In this article, the weighted L1 norm constraint model is closer to the L0 norm constraint model through imaging comparison of the four algorithms in the case of no noise and -2dB Gaussian white noise. In other words, the quality and stability of the imaging are improved by improving the weighting function.