空时自适应杂波分类抑制与弱小运动目标检测
DOI:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

V243

基金项目:

国家863高技术计划(2004AA823120),国家自然科学基金(10376005)资助项目.


SPATIAL-TEMPORAL ADAPTIVE CLUTTER CLASSIFICATION SUPPRESSION AND DIM SMALL MOVING TARGETS DETECTION
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    提出了一种新的方法应用于一类重要的高维信号检测问题:在强杂波干扰下检测数字图像序列中位置和速度未知的弱小运动目标.通过对输入序列时域灰度矩进行学习,将像素分成两类——静杂波和动杂波,分别对其采用非参数时域滤波和LS自适应滤波进行去除,从而将原始数据转化为准SPGWN模型.杂波抑制后,根据单帧多像素目标模型假设,采用在空、时域联合集成信号能量的检测算法,能有效地改善信噪比并且有利于实时实现,理论分析和对真实数据的大量仿真试验验证了本方法的有效性。

    Abstract:

    A new method was proposed for the solution of an important class of multidimensional signal detection problems: the detection of dim,small and moving targets of unknown position and velocity in heavy clutter in a sequence of digital images.By studying temporal gray-level moment of input sequence,the pixels were classified into two categories: stationary clutter and variational clutter.And a nonparametric temporal filter and a LS adaptive filter were applied for suppressing clutter respectively,thus the raw images were transformed into quasi SPGWN model.Then according to a target model of multi-pixel per frame,a detection algorithm integrating signal energy in spatial and temporal domain jointly was employed.The algorithm can improve SNR evidently and can easily be implemented in real time.The theoretic analysis and many simulations of real data verify the validity of the method.

    参考文献
    相似文献
    引证文献
引用本文

吴宏刚 李晓峰 陈跃斌 李在铭.空时自适应杂波分类抑制与弱小运动目标检测[J].红外与毫米波学报,2006,25(4):301~305]. WU Hong-Gang, LI Xiao-Feng, CHEN Yue-Bin, LI Zai-Ming. SPATIAL-TEMPORAL ADAPTIVE CLUTTER CLASSIFICATION SUPPRESSION AND DIM SMALL MOVING TARGETS DETECTION[J]. J. Infrared Millim. Waves,2006,25(4):301~305.]

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2005-08-15
  • 最后修改日期:2006-01-11
  • 录用日期:
  • 在线发布日期:
  • 出版日期:
文章二维码