{"title":"Optimal array signal processing in unknown noise environments via parametric approaches","authors":"Qiang Wu, K. M. Wong, J. Reilly","doi":"10.1117/12.130934","DOIUrl":null,"url":null,"abstract":"Under the assumption that noise correlation is spatially limited, using two separated arrays, the authors propose a new parametric approach for consistent directions-of-arrival estimations in unknown noise environments. The theoretical performance analysis of the proposed DOA estimations is presented. With the use of the theoretical performance, the best weighting matrices of the parametric criteria have been derived. More significantly, it has been shown that within the best weighted criteria, using canonical decomposition, one can achieve optimal performance among a large set of eigendecompositions.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.130934","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Under the assumption that noise correlation is spatially limited, using two separated arrays, the authors propose a new parametric approach for consistent directions-of-arrival estimations in unknown noise environments. The theoretical performance analysis of the proposed DOA estimations is presented. With the use of the theoretical performance, the best weighting matrices of the parametric criteria have been derived. More significantly, it has been shown that within the best weighted criteria, using canonical decomposition, one can achieve optimal performance among a large set of eigendecompositions.<>
在噪声相关性空间有限的假设下,作者利用两个分离的阵列,提出了一种新的参数方法,用于在未知噪声环境中进行一致的到达方向估计。文中对所提出的 DOA 估计进行了理论性能分析。利用理论性能,得出了参数标准的最佳加权矩阵。更重要的是,研究表明,在最佳加权准则范围内,利用规范分解,可以在大量的eigendecompositions中获得最佳性能。