{"title":"Reconstruction of HRRP with missing data and noise","authors":"I. Jouny","doi":"10.1109/AP-S/USNC-URSI47032.2022.9887193","DOIUrl":null,"url":null,"abstract":"This paper applies a recently developed technique for Bayesian reconstruction of Fourier pairs to reconstruct the impulse response (or the High Range Resolution Profile-HRRP) of a stepped-frequency radar target assuming missing data (backscatter), and assuming additive Gaussian noise corrupting the data. This technique is very successful at recovering HRRP of a target with significant amount of backscatter missing. The reconstructed data is used for target identification with performance nearly close to the optimal classifier.","PeriodicalId":371560,"journal":{"name":"2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper applies a recently developed technique for Bayesian reconstruction of Fourier pairs to reconstruct the impulse response (or the High Range Resolution Profile-HRRP) of a stepped-frequency radar target assuming missing data (backscatter), and assuming additive Gaussian noise corrupting the data. This technique is very successful at recovering HRRP of a target with significant amount of backscatter missing. The reconstructed data is used for target identification with performance nearly close to the optimal classifier.
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