The ISAR imaging of ballistic midcourse targets based on Sparse Bayesian Learning

摘要

The ISAR (inverse synthetic aperture radar) imaging technology is an important tool for the ballistic missile midcourse target recognitions. Considering the rotationally symmetric targets, the sparse representation model of the ballistic midcourse targets with micro-motion is established. The sparse recovery algorithm named SBL (Sparse Bayesian Learning) is analyzed, which can provide a much sparser solution than the general sparse recovery algorithms. Based on the newly developed CS (Compress sensing) theory, the ISAR imaging of the ballistic missile is reconstructed by using only a few echoes. Simulation results verify the validity and superiority of the proposed method.