ISAR Imaging of Target Exhibiting Micro-Motion Components Based on Joint Constraints of Low Rank and Structured Sparsity

The inverse synthetic aperture radar (ISAR) can obtain high-resolution ISAR image of moving aerial targets. However, the micro-Doppler (m-D) interference generated by the target exhibiting micro-motion components can lead to a locally defocused ISAR image. Moreover, the echoes received by the receiver of the radar may be missing due to the environment for the propagation of electromagnetic waves and the switching of the frequency band of the radar, where this leads to a sparse aperture. A sparse aperture can result in severe interference from sidelobe or grating lobe in the ISAR image. To solve these problems, this article proposes a structured prior model based on the joint constraints of the nuclear norm,
$l_{1}$
norm, and
$l_{2,1}$
norm. The nuclear norm and the
$l_{1}$
norm are used to constrain the low rank and sparsity of the high-resolution range profile (Hrrp) sequence of the target, while the
$l_{2,1}$
norm is used to constrain the structured features of the ISAR image of the main body. Furthermore, we use the linear alternating direction method of multipliers (LADMMs) to solve the model and obtain the final ISAR image of the main body. The proposed method based on the above constraints can not only eliminate interference from the sidelobe and grating lobe as well as m-D interference but also retain the structured features and connectivity of the ISAR image of the main body. The performance of the proposed method is verified based on data from simulations and experimental measurements.