DOA Estimation for Underwater Acoustic Sensor Arrays Using Jensen-Bregman LogDet Divergence on Positive Definite Matrix Manifolds

doi:10.12382/bgxb.2024.0225

Abstract

Abstract:

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.

Key words: ocean, direction of arrival estimation, Jensen-Bregman LogDet divergence, matrix manifold, matrix information geometry

CLC Number:

TN911

WANG Zhuying, YAN Yongsheng, ZHANG Hongwei, SUO Jian, HE Ke, WANG Haiyan. DOA Estimation for Underwater Acoustic Sensor Arrays Using Jensen-Bregman LogDet Divergence on Positive Definite Matrix Manifolds[J]. Acta Armamentarii, 2025, 46(2): 240225-.

Figures/Tables 9

Fig.1 Geometric distances corresponding to different angles

Fig.2 Geometric distance corresponding to multiple incident sources

Fig.3 RMSEs of DOA estimation using different algorithms versus SNR

Fig.4 RMSEs of DOA estimation using different algorithms versus the number of snapshots

Fig.5 RMSEs of DOA estimation of different algorithms versus the number of array elements

Fig.6 Schematic diagram of anechoic pool experiment

Fig.7 Pool experimental equipment

Fig.8 Time domain and frequency domain analysis of single array element received data

Fig.9 DOA estimation of received data from ULA

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