An Improved Spatially Variant MOCO Approach Based on an MDA for High-Resolution UAV SAR Imaging with Large Measurement Errors

For unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imaging, motion errors cannot be obtained accurately when high precision motion sensors are not equipped on the platform. This means that traditional data-based motion compensation (MOCO) cannot be directly implemented due to large measurement errors. In addition, classic autofocusing techniques, such as phase gradient autofocus (PGA) or map-drift algorithm (MDA), do not perform well with spatially variant errors, greatly affecting the imaging qualities, especially for high-resolution and large-swath cases. In this study, an improved spatially variant MOCO approach based on an MDA is developed to effectively eliminate the spatially variant errors. Based on the coarse and precise MDA chirp rate error estimation, motion errors are optimally acquired by the random sample consensus (RANSAC) iteration. Two-dimensional (2D) mapping is used to decouple the spatially variant residual errors into two linear independent dimensions so that the chirp-z transform (CZT) can be performed for echo data correction. Unlike traditional approaches, the spatially variant components can be compensated without any measured motion information, which indicates that the proposed approach can be applied to the common UAV SAR system with significant measurement errors. Simulations and real data experiments were used to evaluate the performance of the proposed method. The simulation results show that the proposed algorithm is able to effectively minimize spatially variant errors and generate much better imaging results.