文章摘要
刘建设,殷敬伟,朱广平,刘冰.冰下运动目标主动探测技术研究*[J].,2019,38(4):562-568
冰下运动目标主动探测技术研究*
Research on technologies of active sonar moving target detection in under-ice waters
投稿时间:2019-02-26  修订日期:2019-06-29
中文摘要:
      使用主动声呐探测冰下目标时,混响是无法回避的问题。在大面积冰层覆盖的水域中,混响具有强度大、作用范围广的特点,导致主动声呐对目标探测的性能大大下降。本文针对有着强混响的冰下水域这一应用背景,重点研究了基于低秩矩阵恢复理论的冰下运动目标主动探测方法。该方法运用常规波束形成获取多组方位时间历程图像,然后利用低秩矩阵恢复算法分离图像中的混响背景与运动目标。仿真表明不精确的增广拉格朗日乘子法(Inexact Augmented Lagrange Multiplier, IALM)在等间隔直线阵、非等距线阵和均匀间隔圆形阵三种阵型下均有较好的效果。同时与经典的背景差分法做了对比,表明了低秩矩阵恢复算法具有更好的适应性。松花江冰下实验验证了在线阵情况下低秩矩阵恢复算法的实际应用性。
英文摘要:
      Reverberation in under-ice waters is a question that can’t be evaded when an active sonar detects targets. In a large area of ice-covered, reverberation has the characteristics of strong strength and wide scope, which result in poor performance of the active sonar to target detection. In this paper, moving target detection method based on low rank matrix recovery was studied against strong reverberation in under-ice waters. We used conventional beamforming to obtain multi-group time-bearing images and then separated reverberation background and moving targets through low rank matrix recovery. Simulation showed that the inexact augmented Lagrange multiplier method (IALM) is effective in the case of ULA, MGA and UCA. The comparison with classical background difference method showed that the low-rank matrix recovery algorithm has better adaptability. The experiment on the Songhua river verified applicability of the low-rank matrix recovery algorithm.
DOI:10.11684/j.issn.1000-310X.2019.04.013
中文关键词: 主动声呐,低秩矩阵恢复,冰下混响,运动目标探测
英文关键词: Active sonar, Low rank matrix recovery, Reverberation under ice, Moving target detection
基金项目:国家重点研发计划(2018YFC1405900),国家自然科学基金(61631008,51779061),霍英东教育基金(151007),黑龙江省杰出青年科学基金(JC2017017).
作者单位E-mail
刘建设 哈尔滨工程大学水声工程学院 2247573393@qq.com 
殷敬伟 哈尔滨工程大学水声工程学院 yinjingwei@hrbeu.edu.cn 
朱广平 哈尔滨工程大学水声工程学院 guangpingzhu@hrbeu.edu.cn 
刘冰 哈尔滨工程大学水声工程学院 liubing09513@hrbeu.edu.cn 
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