逆合成孔径雷达
计算机科学
稳健性(进化)
图像融合
人工智能
贝叶斯概率
贝叶斯推理
合成孔径雷达
先验概率
图像形成
迭代重建
算法
后验概率
反问题
最大后验估计
压缩传感
雷达成像
计算机视觉
模式识别(心理学)
雷达
数学
图像(数学)
数学分析
基因
电信
生物化学
化学
作者
Zhu Xiaoxiu,Shang Chaoxuan,Baofeng Guo,Lin Shi,Wenhua Hu,Huiyan Zeng
标识
DOI:10.1117/1.jrs.14.036511
摘要
Images from high-resolution inverse synthetic aperture radar (ISAR) can provide more information about the targets. Multiband fusion imaging techniques can achieve higher range resolution without increasing hardware costs. A multiband fusion imaging algorithm based on variational Bayesian inference (VBI) is proposed to improve the range resolution of ISAR images. First, a multiband fusion ISAR imaging model is established based on sparse representation. Second, the scattering coefficients and noise are assumed to be the Laplacian scale mixture distribution and the complex Gaussian distribution, respectively. Finally, the fusion image is directly reconstructed in the complex domain by the VBI based on Laplace approximation method. The effectiveness and robustness of the proposed algorithm are verified by the experimental fusion results of one-dimensional signals and two-dimensional ISAR images.
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