{"title":"一种改进的线性调频信号重构方法及其应用","authors":"Shan Luo, Qiu Xn, Tong Wu, S. Du","doi":"10.1109/ICIVC.2018.8492719","DOIUrl":null,"url":null,"abstract":"In this paper, a time-domain signal reconstruction method based on the Lv distribution (LVD) is introduced for multi-component linear frequency modulated (LFM) signals. Comparing to the LVD based signal reconstruction (LSR) which had been reported to recover signals based on the auto-terms, our approach can reduce recovery errors by subtracting cross-terms mixed in the auto-terms. Therefore it is an improved method of LSR, referring to as the LSR with suppressed cross-terms (LSRSC). Examples are simulated to show that the LSRSC is able to reconstruct multi-component LFM signals effectively. Finally, the proposed method is employed on re-sampling to arbitrary sampling rate and achieves better performance than the LSR and fractional Fourier transform.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved LFM Signal Reconstruction Method and its Application\",\"authors\":\"Shan Luo, Qiu Xn, Tong Wu, S. Du\",\"doi\":\"10.1109/ICIVC.2018.8492719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a time-domain signal reconstruction method based on the Lv distribution (LVD) is introduced for multi-component linear frequency modulated (LFM) signals. Comparing to the LVD based signal reconstruction (LSR) which had been reported to recover signals based on the auto-terms, our approach can reduce recovery errors by subtracting cross-terms mixed in the auto-terms. Therefore it is an improved method of LSR, referring to as the LSR with suppressed cross-terms (LSRSC). Examples are simulated to show that the LSRSC is able to reconstruct multi-component LFM signals effectively. Finally, the proposed method is employed on re-sampling to arbitrary sampling rate and achieves better performance than the LSR and fractional Fourier transform.\",\"PeriodicalId\":173981,\"journal\":{\"name\":\"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIVC.2018.8492719\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVC.2018.8492719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved LFM Signal Reconstruction Method and its Application
In this paper, a time-domain signal reconstruction method based on the Lv distribution (LVD) is introduced for multi-component linear frequency modulated (LFM) signals. Comparing to the LVD based signal reconstruction (LSR) which had been reported to recover signals based on the auto-terms, our approach can reduce recovery errors by subtracting cross-terms mixed in the auto-terms. Therefore it is an improved method of LSR, referring to as the LSR with suppressed cross-terms (LSRSC). Examples are simulated to show that the LSRSC is able to reconstruct multi-component LFM signals effectively. Finally, the proposed method is employed on re-sampling to arbitrary sampling rate and achieves better performance than the LSR and fractional Fourier transform.