电子学报 ›› 2017, Vol. 45 ›› Issue (2): 291-299.DOI: 10.3969/j.issn.0372-2112.2017.02.005

• 学术论文 • 上一篇    下一篇

稀疏孔径下的运动补偿及快速超分辨成像方法

李少东1, 陈文峰1, 杨军2, 马晓岩2, 吕明久1   

  1. 1. 空军预警学院研究生队, 湖北武汉 430019;
    2. 空军预警学院三系, 湖北武汉 430019
  • 收稿日期:2015-09-30 修回日期:2016-04-21 出版日期:2017-02-25 发布日期:2017-02-25
  • 作者简介:李少东,男,1987年出生于河北保定.空军预警学院博士生.主要研究方向为压缩感知在ISAR中的应用、雷达成像.E-mail:liying198798@126.com;陈文峰,男,1989年出生于新疆伊犁.空军预警学院硕士生.主要研究方向为雷达成像、压缩感知.E-mail:chenwf925@163.com;杨军,男,1973年出生于云南大理.空军预警学院副教授、硕士生导师.主要研究方向为雷达系统、雷达信号处理与检测理、SAR/ISAR成像等;马晓岩,男,1962年出生于湖北赤壁.空军预警学院教授、博士生导师,主要研究方向为雷达系统、雷达信号处理与检测理、现代信号处理及其应用;吕明久,男,1985年出生于安徽庐江.空军预警学院博士生.主要研究方向为步进频雷达成像、压缩感知.E-mail:lv_mingjiu@163.com
  • 基金资助:

    国家自然科学基金(No.61179014)

Research on Motion Compensation and Fast Super Resolution Imaging Method Under Sparse Aperture Condition

LI Shao-dong1, CHEN Wen-feng1, YANG Jun2, MA Xiao-yan2, LV Ming-jiu1   

  1. 1. Deptartment of Graduate Management, Air Force Early Warning Academy, Wuhan, Hubei 430019, China;
    2. Deptartment of No. 3, Air Force Early Warning Academy, Wuhan, Hubei 430019, China
  • Received:2015-09-30 Revised:2016-04-21 Online:2017-02-25 Published:2017-02-25

摘要:

针对稀疏孔径条件下目标运动补偿难和方位稀疏成像算法效率低、分辨率差等问题,本文提出了一种稀疏孔径下的运动补偿和快速超分辨成像方法.首先,通过将运动补偿问题转换为距离频域内的多参数估计问题,基于黄金分割法实现参数的快速估计后同时实现包络对齐和相位校正,从而完成运动补偿;其次,针对补偿后不同距离单元ISAR回波的特征,为实现快速的方位成像,本文提出矩阵形式的Nesterov线性Bregman迭代算法(Matrix form of Nesterov Linearized Bregman Iteration,MNLBI)算法,分析了该算法的基本迭代格式,讨论了加快收敛的原因,并详细分析了该算法的运算量,仿真与实测数据结果验证了本文方法的有效性.

关键词: 稀疏孔径, 逆合成孔径雷达成像, 超分辨, 抗噪性, 线性Bregman迭代

Abstract:

In inverse synthetic aperture radar,the difficulty of motion compensation,the low imaging efficiency and resolution of sparse apertures for non-cooperate targets is a challenge problem.To solve the problem,a novel motion compensation and fast imaging method is proposed in this paper.First,the motion compensation is converted into a multi-parameters estimation problem.In order to accomplish the motion compensation,golden selection search (GSS) is adopted to estimate the multi-parameters.Second,the ISAR echoes' feature changes as range cell changing.To realize azimuth imaging efficiently,a matrix form of Nesterov linearized Bregman iteration (MNLBI) algorithm is proposed and the basic iteration scheme is presented as well.The method to speed up convergence of MNLBI is also given.Finally,the robustness to noise and computation is analyzed.The simulation and real data results show the effectiveness of the proposed method.

Key words: sparse aperture, inverse synthetic aperture radar imaging, super resolution, noise robustness, linearized Bregman iteration

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