Abstract Synthetic Aperture Radar (SAR) systems on small unmanned aerial vehicles (UAVs) are challenged by complex, spatially-variant motion errors, which conventional autofocus methods often fail to adequately correct. Specifically, approaches using fixed sub-block partitioning struggle to adapt to the non-uniform spatial distribution of phase errors, while static sample selection mechanisms lack the flexibility to optimize estimation throughout the iterative process. To overcome these limitations, this paper proposes a novel autofocus algorithm that integrates two key innovations. The first, adaptive slant-range wavenumber sub-block partitioning, dynamically adjusts the division granularity based on the local severity of the phase error, ensuring an optimal trade-off between estimation accuracy and robustness. The second, a sample selection strategy guided by a Dynamic Probability Density Function (DPDF), adaptively modifies sample selection probability during iterations, balancing broad exploration in early stages with focused exploitation of high-quality samples in later stages. Integrated within a fast factorized back-projection (FFBP) framework, the method leverages the coherence between azimuth phase error (APE) and non-systematic range cell migration (NsRCM) for joint estimation and correction. Validation using both simulated and measured UAV SAR data demonstrates that the proposed method significantly enhances focusing quality.
Zhao et al. (Tue,) studied this question.