Sparse Superresolution Imaging for Airborne Forward-Looking Radar with Multiple Frames Space.

Airborne forward-looking radar (AFLR) imaging has attracted a lot of attention in fields of Earth observation, independent of weather and daytime. However, the forward-looking imaging quality is not very high. To solve this problem, a sparse superresolution imaging algorithm for AFLR with multiple frames space is proposed. Firstly, the echo model for AFLR is introduced. Then, a multiple frame space is constructed to better describe the distribution of noise and targets of the imaging scene. Finally, the forward-looking imaging problem is constructed as the optimization problem, and the Bayesian framework is used to perform the superresolution imaging. Simulation results and real data results are given to verify its effectiveness.