{"title":"Interference suppression via orthogonal projections","authors":"K. Abend, H. Subbaram","doi":"10.1109/SSAP.1992.246805","DOIUrl":null,"url":null,"abstract":"A technique for suppressing interference in phased array antennas, called the orthogonal projection algorithm, is described. Its performance is characterized theoretically and validated using Monte-Carlo simulations. An orthonormal basis for the interference subspace is calculated in the absence of target, and interference is suppressed by projection of the steering vector onto the orthogonal complement of the interference subspace. The mean output interference plus noise power is shown to be less than that for the sample matrix inversion algorithm for all finite sample sizes. Near convergence is attained with a number of interference snapshots that approximates the dimensionality of the interference subspace and is less than the number of elements.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"1995 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSAP.1992.246805","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
A technique for suppressing interference in phased array antennas, called the orthogonal projection algorithm, is described. Its performance is characterized theoretically and validated using Monte-Carlo simulations. An orthonormal basis for the interference subspace is calculated in the absence of target, and interference is suppressed by projection of the steering vector onto the orthogonal complement of the interference subspace. The mean output interference plus noise power is shown to be less than that for the sample matrix inversion algorithm for all finite sample sizes. Near convergence is attained with a number of interference snapshots that approximates the dimensionality of the interference subspace and is less than the number of elements.<>
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