【作者】 聂瑞欣； 孙超； 刘雄厚；

【Author】 NIE Ruixin;SUN Chao;LIU Xionghou;School of Marine Science and Technology, Northwestern Polytechnical University;Shaanxi Key Laboratory of Underwater Information Technology;

【机构】 西北工业大学航海学院； 陕西省水下信息技术重点实验室；

【摘要】 针对浅海强混响背景下动目标探测困难的问题,本文基于浅海海底混响相关性与目标的运动特性,提出了一种利用加权鲁棒主成分分析(Robust Principal Component Analysis,RPCA)的混响抑制方法。将基阵接收回波经波束形成和匹配滤波后的多个脉冲周期输出视作观测空间,利用高阶空隙度特征引入权值先验来构造新的加权RPCA模型,通过矩阵分解实现了混响抑制。利用数值仿真将该方法与基于传统RPCA的混响抑制方法做了对比,仿真结果表明所提方法可抑制混响并凸显运动目标的亮点特征,有利于提高声呐探测能力。更多还原

【Abstract】 Aiming at the difficulty of moving target detection in strong reverberation background in shallow water, a reverberation suppression method based on weighted robust principal component analysis(weighted-RPCA) is proposed in this paper, which is based on the correlation of reverberation in shallow water and the motion characteristics of target. The multi-ping outputs of the array received echo after CBF and MF are regarded as the observation space, and a new weighted-RPCA model is constructed by using the high-order lacunarity characteristics to introduce the weight prior, and the reverberation suppression is realized by matrix decomposition. The simulation results show that the proposed method can suppress the reverberation and highlight the bright features of moving targets, which is conducive to improving the detection ability of sonar.更多还原