{"title":"基于改进分数阶傅里叶变换的LFM信号参数估计","authors":"Guanyu Qiao, D. Dai, Caikun Zhang","doi":"10.1145/3529570.3529607","DOIUrl":null,"url":null,"abstract":"Given the current computational complexity and the unsatisfactory anti-noise performance of the linear frequency modulated (LFM) signal parameter estimation method, this paper proposes a rather innovative and efficient method based on improved fractional Fourier transform (FrFT). This method first obtains the energy distribution of time-frequency domain (TFD) through short-time Fourier transform (STFT), which is used to determine the order search range of FrFT. On the other hand, incoherent accumulation is also employed to improve the anti-noise performance under low signal-to-noise-ratio (SNR) environments. Extensive computer simulations verified that the algorithm displays a good anti-noise performance while reducing the amount of calculation.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parameter Estimation of LFM Signal Based on Improved Fractional Fourier Transform\",\"authors\":\"Guanyu Qiao, D. Dai, Caikun Zhang\",\"doi\":\"10.1145/3529570.3529607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Given the current computational complexity and the unsatisfactory anti-noise performance of the linear frequency modulated (LFM) signal parameter estimation method, this paper proposes a rather innovative and efficient method based on improved fractional Fourier transform (FrFT). This method first obtains the energy distribution of time-frequency domain (TFD) through short-time Fourier transform (STFT), which is used to determine the order search range of FrFT. On the other hand, incoherent accumulation is also employed to improve the anti-noise performance under low signal-to-noise-ratio (SNR) environments. Extensive computer simulations verified that the algorithm displays a good anti-noise performance while reducing the amount of calculation.\",\"PeriodicalId\":430367,\"journal\":{\"name\":\"Proceedings of the 6th International Conference on Digital Signal Processing\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th International Conference on Digital Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3529570.3529607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Digital Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3529570.3529607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter Estimation of LFM Signal Based on Improved Fractional Fourier Transform
Given the current computational complexity and the unsatisfactory anti-noise performance of the linear frequency modulated (LFM) signal parameter estimation method, this paper proposes a rather innovative and efficient method based on improved fractional Fourier transform (FrFT). This method first obtains the energy distribution of time-frequency domain (TFD) through short-time Fourier transform (STFT), which is used to determine the order search range of FrFT. On the other hand, incoherent accumulation is also employed to improve the anti-noise performance under low signal-to-noise-ratio (SNR) environments. Extensive computer simulations verified that the algorithm displays a good anti-noise performance while reducing the amount of calculation.