A Training-Free Subspace Suppression Method Based on Periodic Rearrangement for LFM Interference in SAR Imaging

To suppress striping artifacts and ghost targets induced by periodic Linear Frequency Modulation (LFM) interference in Synthetic Aperture Radar (SAR) imaging, this letter proposes a training-free four-stage subspace suppression scheme based on periodic rearrangement. By reorganizing the echo data according to the estimated interference period, a low-dimensional interference subspace with near–zero-phase dominance is constructed in the matrix domain, enabling the design of an orthogonal projection operator for selective interference removal. Owing to its subspace-based formulation, the proposed approach requires no data-driven learning or offline training, and it is well suited for practical implementation. Under extremely severe interference (SIR = -20 dB), the proposed method achieves up to a +11.86 dB gain in IRR compared with the reference methods, while reducing the EVM to 1.13%, effectively suppressing residual striping and preserving the target mainlobe response and fine structural details. These results demonstrate that the proposed scheme provides a favorable balance between robustness, target fidelity, and computational efficiency for SAR imaging in complex electromagnetic environments.