Jensen-Bregman LogDet 散度在正定矩阵流形上的水声传感器阵列 DOA 估计

Zhuying Wang, Yongsheng Yan, Hongwei Zhang, Jian Suo, Ke He, Haiyan Wang

科研成果: 期刊稿件文章同行评审

摘要

Because the covariance matrix is a nonlinear space,the traditional method using Euclidean space does not reflect the difference between covariance matrices,resulting in information loss. To address this issue,a DOA estimation method based on Jensen-Bregman LogDet divergence (JBLD) is proposed, which transforms the target orientation estimation problem into the geometric distance problem between two points on the matrix manifold. It is concluded that the angle corresponding to the minimum geometric distance is the incidence angle of target,and two robust matrix manifolds are constructed to complete the establishment of matrix information DOA estimation theory model. The proposed method is verified by simulation and measured data. The results show that the proposed method has better estimation accuracy in low SNR environment than the existing MVDR and MUSIC algorithms. The proposed method has specific practical significance and application prospects,and can provide a solid technical support for underwater target positioning in marine defence and the civil field.

投稿的翻译标题DOA Estimation for Underwater Acoustic Sensor Arrays Using Jensen-Bregman LogDet Divergence on Positive Definite Matrix Manifolds
源语言繁体中文
文章编号240225
期刊Binggong Xuebao/Acta Armamentarii
46
2
DOI
出版状态已出版 - 28 2月 2025

关键词

  • direction of arrival estimation
  • Jensen-Bregman LogDet divergence
  • matrix information geometry
  • matrix manifold
  • ocean

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