Ming Long, Jun Yang, Mingjiu Lv, Wenfeng Chen, Saiqiang Xia
{"title":"低信噪比下转子目标的微运动信号时频结果反演","authors":"Ming Long, Jun Yang, Mingjiu Lv, Wenfeng Chen, Saiqiang Xia","doi":"10.1049/rsn2.12536","DOIUrl":null,"url":null,"abstract":"<p>A signal time-frequency results inversion method is proposed for extracting micro-motion features of rotor targets under low signal-to-noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro-motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time-frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time-frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time-frequency results containing sinusoidal modulation based on the flash's features in the time-frequency results. Based on the inversion time-frequency results, the GS-IRadon algorithm is used to extract micro-motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time-frequency results under low SNRs, providing a new technical approach for micro-motion feature extraction. Time-frequency results inversion is a novelty method used to achieve micro-motion feature extraction of rotor targets.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12536","citationCount":"0","resultStr":"{\"title\":\"Micro-motion signal time-frequency results inversion of rotor targets under low signal-to-noise ratios\",\"authors\":\"Ming Long, Jun Yang, Mingjiu Lv, Wenfeng Chen, Saiqiang Xia\",\"doi\":\"10.1049/rsn2.12536\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A signal time-frequency results inversion method is proposed for extracting micro-motion features of rotor targets under low signal-to-noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro-motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time-frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time-frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time-frequency results containing sinusoidal modulation based on the flash's features in the time-frequency results. Based on the inversion time-frequency results, the GS-IRadon algorithm is used to extract micro-motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time-frequency results under low SNRs, providing a new technical approach for micro-motion feature extraction. Time-frequency results inversion is a novelty method used to achieve micro-motion feature extraction of rotor targets.</p>\",\"PeriodicalId\":50377,\"journal\":{\"name\":\"Iet Radar Sonar and Navigation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-01-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12536\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iet Radar Sonar and Navigation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/rsn2.12536\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Radar Sonar and Navigation","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/rsn2.12536","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Micro-motion signal time-frequency results inversion of rotor targets under low signal-to-noise ratios
A signal time-frequency results inversion method is proposed for extracting micro-motion features of rotor targets under low signal-to-noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro-motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time-frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time-frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time-frequency results containing sinusoidal modulation based on the flash's features in the time-frequency results. Based on the inversion time-frequency results, the GS-IRadon algorithm is used to extract micro-motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time-frequency results under low SNRs, providing a new technical approach for micro-motion feature extraction. Time-frequency results inversion is a novelty method used to achieve micro-motion feature extraction of rotor targets.
期刊介绍:
IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications.
Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.