Research on an inverse synthetic aperture radar imaging algorithm based on non-convex regularization model

Inverse synthetic aperture radar(ISAR) is widely used in military and civilian fields because of its ability to image non-cooperative maneuvering targets. Researches show that the compressed sensing technology can be used to improve the resolution and reduce the amount of data required on the ISAR imaging. In this paper, we focus on a classical non-convex regularization model in the field of compressed sensing. For this model, we propose a new algorithm which is based on the MM iteration algorithm framework and adopts the idea of support shrinkage technique, called as iteration support shrinkage algorithm. The new algorithm is simple and efficient, and numerical experiments show that it performs well in ISAR imaging.