Qingping Liu, Yongqiang Cheng, Kang Liu, Hongqiang Wang
{"title":"基于l1正则化最小二乘的复杂目标雷达前视成像方法","authors":"Qingping Liu, Yongqiang Cheng, Kang Liu, Hongqiang Wang","doi":"10.1109/APCAP56600.2022.10069746","DOIUrl":null,"url":null,"abstract":"Based on the wavefront modulation technique, we introduced the principle and imaging model of forward-looking imaging. Considering the scattering coefficient distribution of the complex target is no longer sparse, L1-regularized least squares method based on sparse representation is proposed for reconstructing radar images of complex targets. The transformation matrix is generated by the dictionary learning method which provides a sparser representation of the scattering coefficient distribution of the complex target. By inversing the solved sparse vector under the transformation matrix, the reconstruction of complex targets can be realized. Simulation results show the effectiveness of the proposed method.","PeriodicalId":197691,"journal":{"name":"2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radar Forward-looking Imaging Method for Complex Targets Based on L1-Regularized Least Squares\",\"authors\":\"Qingping Liu, Yongqiang Cheng, Kang Liu, Hongqiang Wang\",\"doi\":\"10.1109/APCAP56600.2022.10069746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the wavefront modulation technique, we introduced the principle and imaging model of forward-looking imaging. Considering the scattering coefficient distribution of the complex target is no longer sparse, L1-regularized least squares method based on sparse representation is proposed for reconstructing radar images of complex targets. The transformation matrix is generated by the dictionary learning method which provides a sparser representation of the scattering coefficient distribution of the complex target. By inversing the solved sparse vector under the transformation matrix, the reconstruction of complex targets can be realized. Simulation results show the effectiveness of the proposed method.\",\"PeriodicalId\":197691,\"journal\":{\"name\":\"2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APCAP56600.2022.10069746\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APCAP56600.2022.10069746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Radar Forward-looking Imaging Method for Complex Targets Based on L1-Regularized Least Squares
Based on the wavefront modulation technique, we introduced the principle and imaging model of forward-looking imaging. Considering the scattering coefficient distribution of the complex target is no longer sparse, L1-regularized least squares method based on sparse representation is proposed for reconstructing radar images of complex targets. The transformation matrix is generated by the dictionary learning method which provides a sparser representation of the scattering coefficient distribution of the complex target. By inversing the solved sparse vector under the transformation matrix, the reconstruction of complex targets can be realized. Simulation results show the effectiveness of the proposed method.