A bistatic inverse synthetic aperture radar sparse aperture high‐resolution imaging algorithm with migration compensation

Abstract

In recent years, bistatic inverse synthetic aperture radar (Bi‐ISAR) imaging of targets with sparse aperture has attracted more and more attention. One of the critical issues is that the Bi‐ISAR imaging of some large targets with rotational motions is prone to migration through resolution cells, which increases the imaging difficulty. In order to enhance the compensation for through resolution cell migration in those scenarios and improve the accuracy and resolution of Bi‐ISAR images under sparse aperture, that is, improving the target recognition of Bi‐ISAR, the authors aimed at the high‐order migration term and the high‐order phase error term and proposed a Bi‐ISAR sparse aperture high‐resolution imaging algorithm based on complex Bayesian compressed sensing and frequency resampling. The algorithm was solved by ‘distributed’ iteration. The migration compensation under the sparse aperture conditions was completed, and the quality of the reconstructed images was improved. Simulation results verified the effectiveness and superiority of the proposed algorithm.

Novel approach for sparse aperture inverse synthetic aperture radar imaging via improved alternating direction method of multipliers algorithm

“…The inverse synthetic aperture radar (ISAR) has received lots of attention in many areas such as defence, for the reason that it can obtain the image of non-cooperative moving targets regardless of the weather and time [1][2][3]. The high azimuth resolution in the ISAR image is based on the target motion and the coherent processing interval (CPI), while the high range resolution of the ISAR image is obtained by processing the transmitted wide band signal [4].…”

“…Sparse aperture echo will occur under the following conditions: (1) Some radar echoes may be affected by strong noise or interference which needs them to be discarded [6]. (2) In modern radar systems, a radar usually has multiple functions. The radar resources have to periodically or nonperiodically switch to accomplish different tasks, such as target detection, tracking, imaging, and so on [7][8][9].…”

“…All the ADMM based ISAR imaging algorithms mentioned above have the following problems: (1) The parameters of the ADMM are determined either artificially or time-consumingly like [19]. (2) The target and non-target components in the ISAR image share the same model parameters which means that these algorithms do not discriminate the target and non-target components. In this paper, we propose an improved ADMM (I-ADMM) based SA-ISAR imaging algorithm to solve these problems.…”