Abstract. 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.

中文翻译：

---

基于变分贝叶斯推理的多波段融合逆合成孔径雷达成像

摘要。来自高分辨率逆合成孔径雷达 (ISAR) 的图像可以提供有关目标的更多信息。多波段融合成像技术可以在不增加硬件成本的情况下实现更高的距离分辨率。为了提高ISAR图像的距离分辨率，提出了一种基于变分贝叶斯推理(VBI)的多波段融合成像算法。首先，建立了基于稀疏表示的多波段融合ISAR成像模型。其次，假设散射系数和噪声分别为拉普拉斯尺度混合分布和复高斯分布。最后，基于拉普拉斯近似方法，通过VBI直接在复域重建融合图像。