Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT

Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. for datasets with low signal-to-noise ratio (SNR) important but challenging. In this work, we proposed noise-robust translational method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the as fourth-order according to order-of-magnitude analysis, and then design HLPT-GSCFT translation parameter estimation parametric compensation. Specifically, HLPT designed estimate acceleration third-order GSCFT introduced second-order acceleration. Both have strong ability cross-term suppression. addition, use minimum weighted entropy algorithm velocity motion, which can improve noise robustness estimation. Experimental results measured dataset prove that effective noise-robust.