利用独立分量分析进行脉冲分离

Jaron Lin, Jordan Juliano, Alex Erdogan, K. George
{"title":"利用独立分量分析进行脉冲分离","authors":"Jaron Lin, Jordan Juliano, Alex Erdogan, K. George","doi":"10.1109/UEMCON51285.2020.9298098","DOIUrl":null,"url":null,"abstract":"This paper intends to demonstrate pulse separation using independent component analysis (ICA). Independent component analysis is related to the blind source separation (BSS) problem when independent components or the original signals are unknown or blind. BSS is explained by the cocktail party problem where groups of people have separate conversations at a cocktail party, and an individual is selectively extracting a conversation from a mix of conversations. The blind source separation problem is like deinterleaving signals. When a radar receiver intercepts multiple signals, the signals go through interleaving, where the waveforms mix with each other. The process of separating the mixed signals is called deinterleaving. In the radar system, finding the pulse descriptor word (PDW) is essential for deinterleaving because the parameters from PDW will help reconstruct the received radar signals. The radar will process the parameters from the PDW to construct pulse trains to help identify all the source signals that were detected by the radar receiver. Independent component analysis is a method to identify all the source signals received by the receiver.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Pulse Separation Using Independent Component Analysis\",\"authors\":\"Jaron Lin, Jordan Juliano, Alex Erdogan, K. George\",\"doi\":\"10.1109/UEMCON51285.2020.9298098\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper intends to demonstrate pulse separation using independent component analysis (ICA). Independent component analysis is related to the blind source separation (BSS) problem when independent components or the original signals are unknown or blind. BSS is explained by the cocktail party problem where groups of people have separate conversations at a cocktail party, and an individual is selectively extracting a conversation from a mix of conversations. The blind source separation problem is like deinterleaving signals. When a radar receiver intercepts multiple signals, the signals go through interleaving, where the waveforms mix with each other. The process of separating the mixed signals is called deinterleaving. In the radar system, finding the pulse descriptor word (PDW) is essential for deinterleaving because the parameters from PDW will help reconstruct the received radar signals. The radar will process the parameters from the PDW to construct pulse trains to help identify all the source signals that were detected by the radar receiver. Independent component analysis is a method to identify all the source signals received by the receiver.\",\"PeriodicalId\":433609,\"journal\":{\"name\":\"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)\",\"volume\":\"90 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UEMCON51285.2020.9298098\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UEMCON51285.2020.9298098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文试图用独立分量分析(ICA)来演示脉冲分离。独立分量分析涉及到独立分量或原始信号未知或盲的情况下的盲源分离问题。BSS可以用鸡尾酒会问题来解释,在鸡尾酒会上,一群人进行单独的谈话,而个人则有选择地从各种谈话中提取谈话。盲源分离问题类似于交叉信号的分离。当雷达接收器截获多个信号时,信号会经过交错,波形会相互混合。分离混合信号的过程称为去交织。在雷达系统中,寻找脉冲描述词(PDW)是去交错的关键,因为PDW中的参数将有助于重建接收到的雷达信号。雷达将处理来自PDW的参数来构建脉冲序列,以帮助识别雷达接收器检测到的所有源信号。独立分量分析是一种识别接收机接收到的所有源信号的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pulse Separation Using Independent Component Analysis
This paper intends to demonstrate pulse separation using independent component analysis (ICA). Independent component analysis is related to the blind source separation (BSS) problem when independent components or the original signals are unknown or blind. BSS is explained by the cocktail party problem where groups of people have separate conversations at a cocktail party, and an individual is selectively extracting a conversation from a mix of conversations. The blind source separation problem is like deinterleaving signals. When a radar receiver intercepts multiple signals, the signals go through interleaving, where the waveforms mix with each other. The process of separating the mixed signals is called deinterleaving. In the radar system, finding the pulse descriptor word (PDW) is essential for deinterleaving because the parameters from PDW will help reconstruct the received radar signals. The radar will process the parameters from the PDW to construct pulse trains to help identify all the source signals that were detected by the radar receiver. Independent component analysis is a method to identify all the source signals received by the receiver.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信