Accelerated ISAR Sparse Imaging Based on Tight Frame and Projection Operator

For the actual inverse synthetic aperture radar (ISAR) system, some received pulses may be invalid or lost in the long coherent processing interval (CPI) due to nonideal conditions, such as target noncooperation or noise interference. Hence, it is usually difficult to obtain well-focused images for such sparse aperture ISAR data. To address this issue, a novel general analysis model with a tight frame for sparse ISAR imaging is proposed in this article, which operates directly on the ISAR image instead of the redundant sparse coefficient. Furthermore, with the help of the projection operator and majorization minimization algorithmic framework, a simple and effective accelerated iterative algorithm with general analysis model (AIA-GAM) is designed to solve the proposed ISAR imaging model, which only performs the simple proximal mapping in each iteration. The experimental results of simulation data and real measured data demonstrate that the proposed method has better imaging performance and faster convergence speed.