Resource optimization scheduling method for netted radar ISAR imaging under sweep jamming

Inverse synthetic aperture radar (ISAR) enables high-resolution imaging of moving targets, extracting critical features such as target structure and scattering characteristics to provide essential support for target identification. In netted ISAR systems, multi-radar collaborative scheduling can overcome the resource limitations of single-radar systems and enhance multi-target imaging capabilities. However, when facing sweep jamming, ISAR imaging suffers from blurring and defocusing effects. Existing radar resource scheduling methods fail to account for these impacts, making it difficult to guarantee multi-target imaging tasks in sweep jamming environments. To address these challenges, combined with sparse aperture ISAR imaging techniques which can achieve high-resolution images under sparse observation conditions, a resource optimization scheduling method for netted ISAR under sweep jamming conditions is proposed in this paper. Leveraging the periodic variation characteristics of sweep jamming, with minimizing mission completion time as the optimization objective, a resource optimization scheduling model is established by comprehensively considering constraints such as jamming-free period observation and imaging resolution requirements. And an improved adaptive neighborhood search algorithm is proposed to solve the resource optimization scheduling model. This algorithm incorporates a target fitness weighted mechanism into the neighborhood solution generation process, while integrating greedy initial solution generation, adaptive neighborhood operator selection, simulated annealing acceptance criteria, stagnation perturbation mechanisms, and observation pulse allocation based on jamming mask. As a result, the sparse observation pulses for each target can be allocated reasonably. Finally, based on the resource scheduling results, target imaging tasks can be completed using sparse aperture ISAR imaging techniques. Simulation results demonstrate that the proposed algorithm significantly improves multi-target imaging performance of netted ISAR systems in sweep jamming scenarios.