航空学报 > 2014, Vol. 35 Issue (12): 3384-3391   doi: 10.7527/S1000-6893.2014.0073

迭代收缩阈值雷达前视成像方法

焦淑红1, 唐琳1, 齐欢1, 刘学2   

  1. 1. 哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001;
    2. 中国人民解放军92941部队, 辽宁 葫芦岛 125000
  • 收稿日期:2014-01-22 修回日期:2014-04-25 出版日期:2014-12-25 发布日期:2014-04-28
  • 通讯作者: 焦淑红 女, 教授, 博士生导师.主要研究方向: 精确制导、无源定位、图像处理. Tel: 0451-82834346 E-mail: jiaoshuhong@hrbeu.edu.cn E-mail:jiaoshuhong@hrbeu.edu.cn
  • 作者简介:唐琳 男, 博士研究生.主要研究方向: 雷达信号处理. Tel: 0451-82834346 Email: tanglinheu@163.com
  • 基金资助:

    国家自然科学基金(61201410)

Iterative Shrinkage Thresholding Radar Forward-looking Imaging Method

JIAO Shuhong1, TANG Lin1, QI Huan1, LIU Xue2   

  1. 1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
    2. The Unit 92941 of PLA, Huludao 125000, China
  • Received:2014-01-22 Revised:2014-04-25 Online:2014-12-25 Published:2014-04-28
  • Supported by:

    National Natural Science Foundation of China (61201410)

摘要:

针对机载多通道雷达的扫描前视成像问题,研究了利用迭代收缩阈值算法实现单个通道前视成像的方法,在此基础上提出一种扩展的多通道迭代收缩阈值算法来解决多通道前视成像问题,在理论上证明其收敛性,并给出加快收敛速度的方法.该算法首先通过对各个通道的加权叠加,获得多通道雷达最小均方误差意义下的最优解;然后利用目标的稀疏表示,获得最优解在相应稀疏约束下的稀疏解.理论分析和仿真实验表明,相对于现有扫描雷达前视成像方法,所提方法在算法稳定性、场景复原能力和噪声抑制能力等方面具有明显的优势.

关键词: 雷达成像, 单脉冲雷达, 反解卷积, 稀疏约束, 迭代收缩阈值算法

Abstract:

Aimed at the airborne multi-channel scanning radar forward looking imaging problem, the mono-channel forward looking imaging method using iterative shrinkage thresholding algorithm is researched. An extended multi-channel iterative shrinkage thresholding algorithm is proposed for multi-channel forward-looking imaging problem. Its convergence is proven theoretically and a speed up method is also given. First, the optimal solution of multi-channel radar in the sense of minimum mean square error is obtained by the weighted superposition of each channel, and then the sparse solution access to the optimal solution in the corresponding sparse constraints is obtained using the target sparse representation. Theoretical analysis and simulation results show that the new method has obvious advantages in algorithm stability, scenes recovery ability and noise immunity compared with the pre-existing scanning radar forward-looking imaging methods.

Key words: radar imaging, monopulse radar, deconvolution, sparse constraint, iterative shrinkage thresholding algorithm

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