Xueli Sheng, Dewen Li, Ran Cao, Xuan Zhou, Jiarui Yin
{"title":"Adaptive interference suppression based on an invariant subspace of matrices matching for a horizontal array in underwater acoustics.","authors":"Xueli Sheng, Dewen Li, Ran Cao, Xuan Zhou, Jiarui Yin","doi":"10.1121/10.0026373","DOIUrl":null,"url":null,"abstract":"<p><p>Passive detection of target-of-interest (TOI) within strong interferences poses a challenge. This paper introduces an adaptive interference suppression based on an invariant subspace of matrix matching. Assume that the TOI-bearing intervals are known. We define a correlation ratio for each eigenvector to obtain the highest one. Then, we use invariant subspace of matrix matching to measure the distance between two invariant projection matrices of this eigenvector. This identifies and removes the eigenvectors associated with TOI. Finally, the remaining eigenvectors are subtracted from the sample covariance matrix to suppress interference and noise. The viability of the proposed method is demonstrated experimentally.</p>","PeriodicalId":73538,"journal":{"name":"JASA express letters","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JASA express letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1121/10.0026373","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Passive detection of target-of-interest (TOI) within strong interferences poses a challenge. This paper introduces an adaptive interference suppression based on an invariant subspace of matrix matching. Assume that the TOI-bearing intervals are known. We define a correlation ratio for each eigenvector to obtain the highest one. Then, we use invariant subspace of matrix matching to measure the distance between two invariant projection matrices of this eigenvector. This identifies and removes the eigenvectors associated with TOI. Finally, the remaining eigenvectors are subtracted from the sample covariance matrix to suppress interference and noise. The viability of the proposed method is demonstrated experimentally.
在强干扰下被动探测感兴趣目标(TOI)是一项挑战。本文介绍了一种基于矩阵匹配不变子空间的自适应干扰抑制方法。假设含有兴趣目标的区间是已知的。我们为每个特征向量定义一个相关比,以获得最高的相关比。然后,我们使用矩阵匹配的不变子空间来测量该特征向量的两个不变投影矩阵之间的距离。这样就能识别并移除与 TOI 相关的特征向量。最后,从样本协方差矩阵中减去剩余的特征向量,以抑制干扰和噪声。实验证明了所提方法的可行性。