{"title":"利用矢量辐射传输实时估计箔条云的RCS","authors":"Jun-Seon Kim, Dong-Wook Seo","doi":"10.1109/TENSYMP55890.2023.10223607","DOIUrl":null,"url":null,"abstract":"This paper employs the vector radiative transfer approach to estimate the real-time radar cross-section of a chaff cloud. Our study focuses on investigating the dynamic radar cross-section of chaff clouds formed within an aircraft-induced chaff corridor. To account for temporal aspects, we assume the chaff cloud's orientation distribution while considering the chaff's intrinsic properties. The estimated results show the vector radiative transfer technique is efficient in capturing the time-varying properties of chaff clouds and swiftly computing substantial quantities of chaff. Additionally, we emphasize the significance of devising an efficient methodology to manage extensive data stemming from chaff clouds, which could potentially amount to millions of data points.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Employing Vector Radiative Transfer to Estimate RCS of Chaff Cloud in Real-Time\",\"authors\":\"Jun-Seon Kim, Dong-Wook Seo\",\"doi\":\"10.1109/TENSYMP55890.2023.10223607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper employs the vector radiative transfer approach to estimate the real-time radar cross-section of a chaff cloud. Our study focuses on investigating the dynamic radar cross-section of chaff clouds formed within an aircraft-induced chaff corridor. To account for temporal aspects, we assume the chaff cloud's orientation distribution while considering the chaff's intrinsic properties. The estimated results show the vector radiative transfer technique is efficient in capturing the time-varying properties of chaff clouds and swiftly computing substantial quantities of chaff. Additionally, we emphasize the significance of devising an efficient methodology to manage extensive data stemming from chaff clouds, which could potentially amount to millions of data points.\",\"PeriodicalId\":314726,\"journal\":{\"name\":\"2023 IEEE Region 10 Symposium (TENSYMP)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Region 10 Symposium (TENSYMP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENSYMP55890.2023.10223607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Region 10 Symposium (TENSYMP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENSYMP55890.2023.10223607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Employing Vector Radiative Transfer to Estimate RCS of Chaff Cloud in Real-Time
This paper employs the vector radiative transfer approach to estimate the real-time radar cross-section of a chaff cloud. Our study focuses on investigating the dynamic radar cross-section of chaff clouds formed within an aircraft-induced chaff corridor. To account for temporal aspects, we assume the chaff cloud's orientation distribution while considering the chaff's intrinsic properties. The estimated results show the vector radiative transfer technique is efficient in capturing the time-varying properties of chaff clouds and swiftly computing substantial quantities of chaff. Additionally, we emphasize the significance of devising an efficient methodology to manage extensive data stemming from chaff clouds, which could potentially amount to millions of data points.