{"title":"A new robust method for 2-D sinusoidal frequency estimation","authors":"S. Oh, N. Srinivasa, R. Kashyap","doi":"10.1109/MDSP.1989.97074","DOIUrl":null,"url":null,"abstract":"Summary form only given, as follows. A new one-dimensional (1-D) frequency estimation method for two-dimensional (2-D) sinusoidal signals is proposed. The received observations are assumed to be a sum of 2-D sinusoidal signals and additive white noise process. The distribution of the noise process is not necessarily Gaussian. Among the family of robust estimates, the M-estimate which is obtained by the minimization of a nonquadratic function of normalized residuals, is considered. It is shown that the new frequency estimates perform as well as a 2-D autoregressive (AR) model based method. Further, in the new estimation method the variance of resulting estimate is guaranteed to be O(N/sup -2/). Numerical simulation studies confirming the performance of the new estimation method are presented.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"34 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth Multidimensional Signal Processing Workshop,","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDSP.1989.97074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Summary form only given, as follows. A new one-dimensional (1-D) frequency estimation method for two-dimensional (2-D) sinusoidal signals is proposed. The received observations are assumed to be a sum of 2-D sinusoidal signals and additive white noise process. The distribution of the noise process is not necessarily Gaussian. Among the family of robust estimates, the M-estimate which is obtained by the minimization of a nonquadratic function of normalized residuals, is considered. It is shown that the new frequency estimates perform as well as a 2-D autoregressive (AR) model based method. Further, in the new estimation method the variance of resulting estimate is guaranteed to be O(N/sup -2/). Numerical simulation studies confirming the performance of the new estimation method are presented.<>