{"title":"物理信息复自然共振频率校正","authors":"Suqin Wu;Yonggang Li;Fusheng Wang;Weigang Zhu;Shuge Wang;Jiahao Wang;Yining Shi;Yunhua Zhang","doi":"10.1109/LAWP.2025.3578384","DOIUrl":null,"url":null,"abstract":"Complex natural resonance frequency is only determined by target’s intrinsic properties, and independent of incident angle and target’s attitude, making target recognition based on complex natural resonance frequency a promising solution. However, the existing extraction methods inevitably introduce calculation errors, resulting in inaccurate extraction of it, which limits the improvement of recognition accuracy. To address these issues, physics-informed complex natural resonance frequency correction is proposed. First, a loss function based on target’s resonance scattering mechanism is designed. Physics is used to drive network to extract features, strengthening the physical constraints on the corrected data. Second, by introducing the idea of complex-valued neural network, a complex domain-variational autoencoder is designed, which considers the correlation between the real and imaginary parts of complex data, thereby excavating more internal features of data. Experimental results verify the effectiveness of the proposed method. Compared with the original data, the relative errors are reduced by 18.02% for the real part and 0.49% for the imaginary part. Improving the consistency between corrected data and target’s resonance scattering mechanism.","PeriodicalId":51059,"journal":{"name":"IEEE Antennas and Wireless Propagation Letters","volume":"24 9","pages":"2959-2963"},"PeriodicalIF":4.8000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Physics-Informed Complex Natural Resonance Frequency Correction\",\"authors\":\"Suqin Wu;Yonggang Li;Fusheng Wang;Weigang Zhu;Shuge Wang;Jiahao Wang;Yining Shi;Yunhua Zhang\",\"doi\":\"10.1109/LAWP.2025.3578384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Complex natural resonance frequency is only determined by target’s intrinsic properties, and independent of incident angle and target’s attitude, making target recognition based on complex natural resonance frequency a promising solution. However, the existing extraction methods inevitably introduce calculation errors, resulting in inaccurate extraction of it, which limits the improvement of recognition accuracy. To address these issues, physics-informed complex natural resonance frequency correction is proposed. First, a loss function based on target’s resonance scattering mechanism is designed. Physics is used to drive network to extract features, strengthening the physical constraints on the corrected data. Second, by introducing the idea of complex-valued neural network, a complex domain-variational autoencoder is designed, which considers the correlation between the real and imaginary parts of complex data, thereby excavating more internal features of data. Experimental results verify the effectiveness of the proposed method. Compared with the original data, the relative errors are reduced by 18.02% for the real part and 0.49% for the imaginary part. Improving the consistency between corrected data and target’s resonance scattering mechanism.\",\"PeriodicalId\":51059,\"journal\":{\"name\":\"IEEE Antennas and Wireless Propagation Letters\",\"volume\":\"24 9\",\"pages\":\"2959-2963\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Antennas and Wireless Propagation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11029597/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Antennas and Wireless Propagation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11029597/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Physics-Informed Complex Natural Resonance Frequency Correction
Complex natural resonance frequency is only determined by target’s intrinsic properties, and independent of incident angle and target’s attitude, making target recognition based on complex natural resonance frequency a promising solution. However, the existing extraction methods inevitably introduce calculation errors, resulting in inaccurate extraction of it, which limits the improvement of recognition accuracy. To address these issues, physics-informed complex natural resonance frequency correction is proposed. First, a loss function based on target’s resonance scattering mechanism is designed. Physics is used to drive network to extract features, strengthening the physical constraints on the corrected data. Second, by introducing the idea of complex-valued neural network, a complex domain-variational autoencoder is designed, which considers the correlation between the real and imaginary parts of complex data, thereby excavating more internal features of data. Experimental results verify the effectiveness of the proposed method. Compared with the original data, the relative errors are reduced by 18.02% for the real part and 0.49% for the imaginary part. Improving the consistency between corrected data and target’s resonance scattering mechanism.
期刊介绍:
IEEE Antennas and Wireless Propagation Letters (AWP Letters) is devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation. These are areas of competence for the IEEE Antennas and Propagation Society (AP-S). AWPL aims to be one of the "fastest" journals among IEEE publications. This means that for papers that are eventually accepted, it is intended that an author may expect his or her paper to appear in IEEE Xplore, on average, around two months after submission.