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兵工学报 ›› 2015, Vol. 36 ›› Issue (1): 130-137.doi: 10.3969/j.issn.1000-1093.2015.01.019

• 论文 • 上一篇    下一篇

目标回波时频分布的几何结构图像形态特征

李秀坤1,2, 夏峙1,2, 朱旭1,2   

  1. (1.哈尔滨工程大学 水声技术重点实验室, 黑龙江 哈尔滨 150001;
  • 收稿日期:2013-10-02 修回日期:2013-10-02 上线日期:2015-03-14
  • 作者简介:李秀坤(1962—)女教授, 博士生导师
  • 基金资助:
    国家自然科学基金项目(51279033); 黑龙江省自然科学基金项目(F201346)

Image Morphological Characteristics of Geometrical Structure of Target Echo Time-frequency Distribution

LI Xiu-kun1,2, XIA Zhi1,2, ZHU Xu1,2   

  1. (1.Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, Heilongjiang, China;2College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China)
  • Received:2013-10-02 Revised:2013-10-02 Online:2015-03-14

摘要: 沉底目标识别的关键在于从混响背景中提取出稳定的目标回波信号特征。目标的棱角散射能够携带目标的几何形状信息,在时频域内具有较规则的分布特性,但是各棱角散射回波声程差小,而且淹没在混响中。已有的时频分析方法局限于其抗混响能力及时频分辨力,为得到一种有效的目标几何特征提取方法,提出将一维几何回波时域信号在二维时频域中的几何分布特征作为图像特征进行特征提取,通过研究几何亮点回波和混响在时频平面上的形态特征,构造与几何亮点时频特性匹配的结构元并对时频分布图像进行形态滤波。通过仿真与湖试数据分析,相比已有方法,文中所提方法能够在实现目标几何亮点结构的识别同时进一步抑制混响。

关键词: 声学, 沉底目标, 几何亮点特征, 混响抑制, 形态滤波

Abstract: The key of underwater bottom target recognition is to extract the stable target echo signal feature from reverberation background. The edge scattering of target carries its geometrical information, and it has regular distribution on the time-frequency plane. But the path difference of edge scattering is little, and it’s always drowned out by reverberation. Because of the limited time-frequency resolution and the limited anti-reverberation ability of existing time-frequency analysis method, there hasn’t been any effective feature extraction method of target geometrical characteristics. The geometrical distribution feature of target echo on the time-frequency plane is extracted as an image feature. The morphological characteristics of geometrical highlight and reverberation on the time-frequency plane are researched. The structural element matched with the time-frequency distribution is constructed, and the time-frequency distribution image is morphologically filtered. The experimental data processing result shows that, compared with existing method, the proposed method can be used to recognize the geometrical highlight structure of target and suppress the reverberation at the same time.

Key words: acoustics, bottom target, geometrical highlight feature, anti-reverberation, morphological filter

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