{"title":"一种循环平稳信号的自校正算法及其唯一性分析","authors":"P. Yip, Yifeng Zhou","doi":"10.1109/ICASSP.1995.480581","DOIUrl":null,"url":null,"abstract":"A self-calibration DOA estimation algorithm for cyclostationary source signals is presented in which the effects of the sensor gain and phase shift uncertainty have been eliminated. The uniqueness conditions and the asymptotic consistency of the estimates are discussed. An alternating projecting optimization algorithm is provided which lessens the computational load involved in the nonlinear multivariate optimization problem. A numerical example is presented to show the effectiveness of the algorithm.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"16 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A self-calibration algorithm for cyclostationary signals and its uniqueness analysis\",\"authors\":\"P. Yip, Yifeng Zhou\",\"doi\":\"10.1109/ICASSP.1995.480581\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A self-calibration DOA estimation algorithm for cyclostationary source signals is presented in which the effects of the sensor gain and phase shift uncertainty have been eliminated. The uniqueness conditions and the asymptotic consistency of the estimates are discussed. An alternating projecting optimization algorithm is provided which lessens the computational load involved in the nonlinear multivariate optimization problem. A numerical example is presented to show the effectiveness of the algorithm.\",\"PeriodicalId\":300119,\"journal\":{\"name\":\"1995 International Conference on Acoustics, Speech, and Signal Processing\",\"volume\":\"16 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1995 International Conference on Acoustics, Speech, and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.1995.480581\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1995 International Conference on Acoustics, Speech, and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1995.480581","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A self-calibration algorithm for cyclostationary signals and its uniqueness analysis
A self-calibration DOA estimation algorithm for cyclostationary source signals is presented in which the effects of the sensor gain and phase shift uncertainty have been eliminated. The uniqueness conditions and the asymptotic consistency of the estimates are discussed. An alternating projecting optimization algorithm is provided which lessens the computational load involved in the nonlinear multivariate optimization problem. A numerical example is presented to show the effectiveness of the algorithm.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
