{"title":"共素数抽样和互相关估计","authors":"Usham V. Dias, Seshan Srirangarajan","doi":"10.1109/NCC.2018.8600112","DOIUrl":null,"url":null,"abstract":"Research in the field of co-prime arrays and samplers has been mainly focused on reconstructing the autocorrelation and the spectral content of a signal at the Nyquist rate from sub-Nyquist data. This has found applications in power spectrum estimation, beamforming, direction-of-arrival estimation, and system identification. However, the use of coprime samplers for cross-correlation estimation has not received much attention. We describe cross-correlation estimation using co-prime samplers and consider two scenarios. In the first, both signals are acquired using co-prime samplers, while in the second scenario we assume one of the signals to be a known signal and thus available at the Nyquist rate, and the second signal is acquired using a co-prime sampler. We determine the number of contributors available for cross-correlation estimation at each difference value as this is a key parameter in determining the estimation accuracy. The work presented in this paper will have applications in time-delay, range, velocity, acceleration, and cross-spectrum estimation, which require cross-correlation estimation.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Co-Prime Sampling and Cross-Correlation Estimation\",\"authors\":\"Usham V. Dias, Seshan Srirangarajan\",\"doi\":\"10.1109/NCC.2018.8600112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Research in the field of co-prime arrays and samplers has been mainly focused on reconstructing the autocorrelation and the spectral content of a signal at the Nyquist rate from sub-Nyquist data. This has found applications in power spectrum estimation, beamforming, direction-of-arrival estimation, and system identification. However, the use of coprime samplers for cross-correlation estimation has not received much attention. We describe cross-correlation estimation using co-prime samplers and consider two scenarios. In the first, both signals are acquired using co-prime samplers, while in the second scenario we assume one of the signals to be a known signal and thus available at the Nyquist rate, and the second signal is acquired using a co-prime sampler. We determine the number of contributors available for cross-correlation estimation at each difference value as this is a key parameter in determining the estimation accuracy. The work presented in this paper will have applications in time-delay, range, velocity, acceleration, and cross-spectrum estimation, which require cross-correlation estimation.\",\"PeriodicalId\":121544,\"journal\":{\"name\":\"2018 Twenty Fourth National Conference on Communications (NCC)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Twenty Fourth National Conference on Communications (NCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NCC.2018.8600112\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Twenty Fourth National Conference on Communications (NCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCC.2018.8600112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Co-Prime Sampling and Cross-Correlation Estimation
Research in the field of co-prime arrays and samplers has been mainly focused on reconstructing the autocorrelation and the spectral content of a signal at the Nyquist rate from sub-Nyquist data. This has found applications in power spectrum estimation, beamforming, direction-of-arrival estimation, and system identification. However, the use of coprime samplers for cross-correlation estimation has not received much attention. We describe cross-correlation estimation using co-prime samplers and consider two scenarios. In the first, both signals are acquired using co-prime samplers, while in the second scenario we assume one of the signals to be a known signal and thus available at the Nyquist rate, and the second signal is acquired using a co-prime sampler. We determine the number of contributors available for cross-correlation estimation at each difference value as this is a key parameter in determining the estimation accuracy. The work presented in this paper will have applications in time-delay, range, velocity, acceleration, and cross-spectrum estimation, which require cross-correlation estimation.