基于空时混沌分析的海面小弱目标检测

李正周; 曹雷; 邵万兴; 陈文豪; 刘冰

doi:10.3788/OPE.20182601.0193

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基于空时混沌分析的海面小弱目标检测

信息科学 | 更新时间：2020-08-13

- 基于空时混沌分析的海面小弱目标检测
- Detection of small target in sea clutter based on spatio-temporal chaos analysis
- 光学精密工程 2018年26卷第1期页码：193-199
- 作者机构：
  
  1.重庆大学通信工程学院, 重庆 400044
  2.中国科学院光束控制重点实验室, 四川成都 610209
- 作者简介：
  
  [ "李正周(1974-), 男, 重庆垫江人, 博士, 教授, 博士生导师, 2004年于中国科学院光电技术研究所获得博士学位, 主要从事目标探测技术与装备开发方面的研究。E-mail: lizhengzhou@cqu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(61675036);中国科学院光束控制重点实验室基金资助项目(2017LBC006);重庆基础与前沿研究计划项目(CSTC2016JCYJA0193)
- DOI：10.3788/OPE.20182601.0193
  中图分类号： TN99
- 收稿日期：2017-05-12，
  
  录用日期：2017-7-15，
  
  纸质出版日期：2018-01-25
- 稿件说明：
移动端阅览
李正周, 曹雷, 邵万兴, 等. 基于空时混沌分析的海面小弱目标检测[J]. 光学精密工程, 2018,26(1):193-199.

Zheng-zhou LI, Lei CAO, Wan-xing SHAO, et al. Detection of small target in sea clutter based on spatio-temporal chaos analysis[J]. Optics and precision engineering, 2018, 26(1): 193-199.
李正周, 曹雷, 邵万兴, 等. 基于空时混沌分析的海面小弱目标检测[J]. 光学精密工程, 2018,26(1):193-199. DOI： 10.3788/OPE.20182601.0193.

Zheng-zhou LI, Lei CAO, Wan-xing SHAO, et al. Detection of small target in sea clutter based on spatio-temporal chaos analysis[J]. Optics and precision engineering, 2018, 26(1): 193-199. DOI： 10.3788/OPE.20182601.0193.

摘要

为了提升海杂波中小弱目标检测能力，通过研究海杂波在空域和时域上的动态特性，提出基于空时混沌分析的海面小弱目标检测方法。首先，在海杂波混沌动力系统相空间重构的基础上提取海浪序列图像的空域混沌参数和时域混沌参数，验证海杂波在空域与时域具备混沌性；然后，采用径向基函数神经网络学习海杂波的空域混沌重构函数、时域混沌重构函数和空时耦合系数，联合空域和时域混沌特性综合重构海杂波在时间与空间上的传播规律。多种复杂度海面目标检测试验结果表明，与空域混沌和时域混沌方法相比，预测误差可降低10%，检测概率可提高20%，基于空时混沌重构的海杂波抑制能力与小目标检测性都得以显著提升。

Abstract

To improve the performance of the clutter suppression and small target detection

a detection algorithm for a small target in sea clutter was proposed

based on the spatio-temporal chaos analysis. First

the sea clutter phase space was reconstructed as a chaotic dynamical system

and the chaotic parameters of the sea clutter sequence image were extracted to verify that the sea clutter owns chaotic properties in the spatial and temporal domains. Furthermore

the spatial chaotic reconstruction function

the temporal chaotic reconstruction function

and the space-time coupling coefficient were estimated by the radial basis function neural network. Finally

the spatial and temporal chaotic functions were integrated jointly to reconstruct the spreading regularity of the moving sea clutter. Some experiments were induced on the small target in various fluctuating sea clutter

and the experimental results show that the proposed algorithm

can improve the performance of the sea clutter suppression and enhance the small target detection ability

with the prediction error reduced by 10% and the detection probability increased by 20%.

关键词

Keywords

references

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