A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR

CHEN Wen-feng; LI Shao-dong; YANG Jun; MA Xiao-yan

doi:10.3969/j.issn.0372-2112.2018.04.011

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A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR

更新时间：2025-07-16

- A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR
- Acta Electronica Sinica Vol. 46, Issue 4, Pages: 840-848(2018)
- 作者机构：
  
  1. 空军预警学院,湖北,武汉,430019
  2. 解放军93253部队,辽宁,大连,116000
  3. 空军预警学院,湖北,武汉,430019
  4. 解放军93253部队,辽宁,大连,116000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61671469)
- DOI：10.3969/j.issn.0372-2112.2018.04.011
  CLC： TN957.52
- Published Online：25 April 2018，
  
  Published：2018
- 稿件说明：
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CHEN Wen-feng, LI Shao-dong, YANG Jun, et al. A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR[J]. Acta Electronica Sinica, 2018, 46(4): 840-848.
DOI：

CHEN Wen-feng, LI Shao-dong, YANG Jun, et al. A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR[J]. Acta Electronica Sinica, 2018, 46(4): 840-848. DOI： 10.3969/j.issn.0372-2112.2018.04.011.

摘要

双基地ISAR成像分辨率受限于信号带宽和方位积累时间，且成像质量受噪声影响严重.本文在充分考虑回波的二维联合稀疏特征基础上，提出二维联合字典下的矩阵复数近似消息传递超分辨快速成像算法.在构建双基地ISAR的二维联合超分辨成像模型基础上，首先通过向量化处理，将二维超分辨成像问题转换为复数基追踪去噪问题；其次通过两种策略实现复数基追踪去噪问题的快速求解，一是利用向量化与Kronecker积的关系，推导出矩阵形式复数近似消息传递算法，从而避免向量化处理带来的大矩阵运算量和大存储量问题；二是为进一步减少单次迭代的运算量，将矩阵乘法等效为二维快速傅里叶变换.最后，利用本文算法在迭代过程中对噪声阈值不断精确逼近，提高算法在低信噪比下的成像能力.仿真数据成像结果验证了本文算法的有效性.

Abstract

In B-ISAR imaging

the range resolution and cross-range resolution are dependent on the signal band and the coherent processing interval

respectively. Generally

the B-ISAR image is seriously affected by noise. In this paper

a matrix form of complex approximate message passing algorithm based on two dimensional coupled dictionaries (MCAMP-TCD) is presented

by considering the 2D coupling sparse feature of the echo. Firstly

the range-azimuth 2D joint B-ISAR imaging model is established. Then the 2D joint super-resolution imaging problem is converted into a complex basis pursuit denoising (C-BPDN) problem through vectorization operation. Secondly

two strategies are implemented to solve C-BPDN problem quickly

the first strategy is utilizing the relation between vectorization operation and Kronecker product to derivate the matrix form of complex approximate message passing algorithm

which can avoid the high computational complexity and memory requirements due to vectorization operation. In second strategy

the two dimensional fast Fourier transform (2D FFT) is introduced to equivalent matrix multiplication

which further reduces the computational complexity of the single iteration. At last

the imaging capability under low signal to noise ratio (SNR) is improved by the ability to accurately approximate the noise threshold of the MCAMP-TCD. Simulation results verify the effectiveness of the proposed method.

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