High-Resolution ISAR Imaging With SSFCS Based on Nonparametric Bayesian Learning and Genetic Algorithm
Yue Wang, Z. Yujie, Xueru Bai
Abstract
For inverse synthetic aperture radar (ISAR) adopting the sparse stepped frequency chirp signal (SSFCS), the target echoes are sparse in the fast time domain with unknown phase errors induced by translational motion, bringing great challenges to well-focused imaging. To tackle this issue, an efficient method for joint motion compensation and high-resolution imaging is proposed under low signal-to-noise ratio (SNR) scenario. Firstly, the signal model is constructed, which is then converted to a probabilistic model with the nonparametric Gamma Process-Complex Gaussian prior. Then, 2D-fast image reconstruction, i.e., 2D-FIR, is proposed for efficient image inference, which avoids matrix inversion by relaxing the lower bound and has high computational efficiency. Finally, a cost function is designed and genetic algorithm is utilized to jointly estimate the translational motion and the 2D image. Experimental results on simulated and measured data have verified the effectiveness of the proposed method.