Sparse Array Configuration Optimization Based on Target Priors in Distributed ISAR Imaging

This letter considers the sparse array configuration optimization problem for long-range distributed inverse synthetic aperture radar (ISAR) imaging to suppress the strong grating lobes. Existing methods face two challenges: 1) The distributed ISAR model requires joint consideration of spatial configuration and temporal sampling, which typical models fail to apply; 2) Existing methods typically suppress grating lobes over the entire angular domain, leading to limited performance gains. To overcome these challenges, we construct an equivalent distributed array model and incorporate target prior information to constrain the angular parameter space, thereby enhancing the grating lobe suppression capability within the region of interest. Simulation results validate that the proposed method improves the peak grating lobe ratio by over 3 dB within the region of interest.