High-resolution ISAR imaging with sparse subband based on waveform fusion dictionary

Abstract

In this paper, a new high-resolution ISAR Imaging method by using sparse subband measurements is developed. It requires no resampling the irregularly measurements onto a uniform frequency grid. Firstly, a one-dimensional waveform dictionary for LFM signal after dechirping is constructed, and the principle of dictionary fusion is illustrated. Then, the two-dimensional waveform fusion dictionary is proposed. Secondly, the fusion imaging method based on Bayesian framework is analyzed, and a hierarchical form of the Laplace prior is used to sparse modeling of the high-resolution ISAR image. Finally, we provide experimental results with one-dimensional and two-dimensional fusion imaging, which illustrated the effectiveness and the superiority of the proposed fusion method over the existing algorithms.