Self-interference Suppression for UUV Flank Array Based on Covariance Matrix Reconstruction

doi:10.12382/bgxb.2024.1081

Abstract

Abstract:

The underwater unmanned vehicle (UUV),as an intelligent underwater operational system,generates a noise during operation,that can significantly interfere with the flank array sonar.The current mainstream adaptive beamforming techniques are primarily designed to suppress the far-field interference and have insufficient adaptability to the strong near-field interference caused by UUV self-interference.To address this issue,this paper proposes a UUV self-interference suppression algorithm based on covariance matrix reconstruction.The covariance matrix of the UUV self-interference exhibits high temporal stability under constant operating conditions due to the rigid connection between the flank array and the UUV.Leveraging this characteristic,the peoposed algorithm first estimates the self-interference covariance matrix with pre-collected data,and then utilizes this matrix to modify the covariance matrix of far-field interference and noise.Consequently,an interference-plus-noise covariance matrix is constructed to compute the weights of the adaptive beamformer.Experimental results indicate that,compared to current mainstream adaptive beamforming methods,the proposed algorithm performs better when the input signal-to-noise ratio exceeds 0dB.Moreover,compared to matrix filtering methods,it can enhance interference suppression effect by approximately 6dB.

Key words: unmanned underwater vehicle, self-interference suppression, array signal processing, passive sonar

CLC Number:

TN911.23

LIANG Guolong, CHEN Yu, QIU Longhao, LI Ying, DU Zhiyao, ZHENG Qingyu. Self-interference Suppression for UUV Flank Array Based on Covariance Matrix Reconstruction[J]. Acta Armamentarii, 2025, 46(11): 241081-.

Figures/Tables 8

Fig.1 Schematic diagram of UUV and its mounted flank array

Fig.2 The simplified models for UUV self-interference and target signal propagation

Fig.3 The comparison of focused beam scanning maps before and after interference suppression (z=0m)

Fig.4 Output SINR versus input SNR without steering vector mismatch

Fig.5 Output SINR versus target azimuth

Fig.6 Output SINR versus input SNR in the case of array element calibration errors

Fig.7 Output SINR versus input SNR in the case of incoherent local scattering

Fig.8 Output SINR versus input SNR in the case of all kinds of random steering vector mismatch

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