A Two-Step Method for Multitarget ISAR Imaging Based on Dual-Precision Optimization

In real-world situations, multiple targets may locate in the same radar antenna beam, resulting in degraded readability of inverse synthetic aperture radar (ISAR) imaging. Due to the different motions of multiple targets, the existing monotarget imaging methods generally lead to tight overlap and severe defocusing. To tackle this problem, this article proposes a two-step method based on dual-precision optimization for multitarget ISAR imaging. First, the Radon transform estimates the range walks of different targets, subsequently generating the proposed coarse motion compensation transforms (MCTs). Moreover, the short imaging interval commonly brings strong similarity within adjacent range profiles. This internal property motivates us to model the multitarget coarse separation as a problem jointly constrained by local similarity and low-rank property. Such a double-constrained problem is then solved by incorporating the alternating direction method of multipliers with linearized alternative direction method with adaptive penalty (ADMM-LADMAP) framework. Through these procedures, range profiles of different targets are partially separated, allowing the correlation-based range alignment methods to estimate the range walk with higher accuracy. With the help of these accurate estimations, MCTs are modified, and the sparsity of the ISAR image is simultaneously enhanced. The sparse prior is thus integrated into the previous optimization to convert coarse separation to a more precise one so as to achieve entire separation. Finally, phase autofocus and cross-range compression are carried out on the completely isolated range profiles to yield well-focused ISAR images. Experiments based on both simulated and measured data demonstrate the effectiveness of the proposed method.