基于知识先验与辅助样本的拖曳声呐空时混响抑制方法

By constructing an optimized model of a set of space-time steering vectors and sparse penalty weights, the problem of reverberation suppression in sonar systems under complex marine environments was solved, achieving accurate modeling and effective suppression of reverberation and improving the detection and estimation performance of sonar systems.

CN121978666BActive Publication Date: 2026-07-17HUNAN UNIV

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN UNIV
Filing Date
2026-04-07
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing sonar systems struggle to effectively suppress reverberation interference in complex marine environments, leading to decreased detection performance and parameter estimation accuracy. In particular, under non-stationary conditions and with limited training samples, existing methods cannot fully utilize the structured distribution characteristics of reverberation and auxiliary training information.

Method used

By constructing a set of space-time steering vectors, filtering vectors in the reverberation energy concentration region, and combining auxiliary samples for energy analysis and sparsity penalty weights, an optimization model is constructed to solve the reverberation sparsity coefficient, reconstructing and suppressing the reverberation component.

Benefits of technology

Accurate modeling and effective suppression of reverberation components were achieved in complex reverberation backgrounds, improving the discriminability and signal-to-noise ratio of target components in residual signals, and enhancing the reliability of sonar target detection and parameter estimation.

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Abstract

本发明公开了一种基于知识先验与辅助样本的拖曳声呐空时混响抑制方法,包括:基于声呐阵列获取辅助样本和待测样本;将空时平面沿空间频率轴和多普勒轴均匀划分后构建空时导向矢量集合;根据空时导向矢量集合筛选位于混响能量集中区域的矢量,得到第一集合;根据辅助样本和第一集合进行能量分析与有效矢量筛选,得到第二集合;为第二集合中各矢量分配稀疏惩罚权重;根据第二集合构建含数据拟合项和加权稀疏约束项的优化模型;求解优化模型得到混响稀疏系数向量;根据混响稀疏系数向量和第二集合得到重构混响分量;将待测样本减去重构混响分量,完成混响抑制。
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