A Novel ISAR Imaging Algorithm for Micromotion Targets Based on Multiple Sparse Bayesian Learning

A novel inverse synthetic aperture radar (ISAR) imaging algorithm for micromotion targets based on multiple sparse Bayesian learning (MSBL) is proposed. First, the signal of the main body is reconstructed by the MSBL method based on the property of its common profile. Subsequently, the signal of rotating parts can be obtained by removing the main body signal from the original signal. Finally, a clear ISAR image of the main body and the micromotion parameter of the rotating parts can be obtained. Numerical results based on simulated and measured data show that the proposed algorithm can not only acquire a clear ISAR image of the main body but also extract the micromotion parameter of the rotating parts effectively.