关键词: 逆合成孔径雷达, 稀疏成像, 伯努利-拉普拉斯, 贝叶斯学习, 马尔可夫链蒙特卡罗

Abstract:

The Laplacian distribution is often used to characterize imaging features in the conventional Bayesian imaging, which makes the image over-sparse. It is easy to lose the weak scattering characteristics of some structural features, which in turn affects the improvement of inverse synthetic aperture radar (ISAR) imaging accuracy. In order to effectively achieve ISAR super-resolution imaging, Bernoulli-Laplace (BL) mixed sparsity priori is adopted in this paper to formulate the statistical characteristics of the target, and the bi-sparsity model is applied to constraint the imaging target prior. Under the Bayesian hierarchical model, the prior is hierarchically constructed by introducing latent variables to simplify the Bayesian inference and reduce the complexity of the model. The problem that the prior distribution and the Gaussian likelihood are not conjugated is solved. In order to avoid tedious manual parameter adjustment, conditional probability functions are established in this paper for random variables, and the Markov chain Monte Carlo (MCMC) sampling algorithm is used for the solution, so that high-dimensional integration can be avoided and analytical posteriors can be obtained. All the hyper-parameters can be fixed automatically, and the proposed algorithm can be performed without too much manual interventions. Both simulated and measured ISAR data validate the effectiveness and superiority of the proposed algorithm.

Key words: inverse synthetic aperture radar (ISAR), sparse imaging, Bernoulli-Laplace (BL), Bayesian learning, Markov chain Monte Carlo (MCMC)