{"title":"利用相空间变换对脉冲无线电UWB跳时脉冲进行盲检测","authors":"Florin Diaconescu","doi":"10.1109/ECAI46879.2019.9042136","DOIUrl":null,"url":null,"abstract":"In this paper, we focus on blind Time of Arrival (ToA) detection and prediction based on time series analysis of the measured samples of an impulse radio UWB time-hopping signal. The ToA detection is based on pattern cluster recognition in time-delay embedding space. This approach has been used for data mining in time series and propose a trade-off between accuracy and computing complexity. Also, the main goal of this study is to identify an estimation algorithm having as little information as possible regarding the received signal.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Blind detection of Impulse Radio UWB Time-Hopping pulses using the phase space transformation\",\"authors\":\"Florin Diaconescu\",\"doi\":\"10.1109/ECAI46879.2019.9042136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we focus on blind Time of Arrival (ToA) detection and prediction based on time series analysis of the measured samples of an impulse radio UWB time-hopping signal. The ToA detection is based on pattern cluster recognition in time-delay embedding space. This approach has been used for data mining in time series and propose a trade-off between accuracy and computing complexity. Also, the main goal of this study is to identify an estimation algorithm having as little information as possible regarding the received signal.\",\"PeriodicalId\":285780,\"journal\":{\"name\":\"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECAI46879.2019.9042136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECAI46879.2019.9042136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Blind detection of Impulse Radio UWB Time-Hopping pulses using the phase space transformation
In this paper, we focus on blind Time of Arrival (ToA) detection and prediction based on time series analysis of the measured samples of an impulse radio UWB time-hopping signal. The ToA detection is based on pattern cluster recognition in time-delay embedding space. This approach has been used for data mining in time series and propose a trade-off between accuracy and computing complexity. Also, the main goal of this study is to identify an estimation algorithm having as little information as possible regarding the received signal.