An Autofocus Approach for UAV-Based Ultrawideband Ultrawidebeam SAR Data With Frequency-Dependent and 2-D Space-Variant Motion Errors

Abstract

Unmanned-aerial-vehicle-based (UAV-based) ultrawideband and ultrawidebeam (UWB) synthetic aperture radar (SAR) is very sensitive to atmospheric turbulence and suffers from serious 2-D space-variant motion errors (SVMEs) caused by the ultrawide beam and frequency-dependent phase errors caused by the ultrawideband. This article proposes an autofocus approach for UAV-based UWB SAR data based on the quasi-polar grid fast factorized backprojection (FFBP) imaging framework, multiple subband local autofocus (MSBLA), and trajectory deviation estimation. First, based on an improved weighted phase gradient autofocus (WPGA) method for subband-division local images, MSBLA is introduced to solve the local motion error estimation problem with frequency-dependent phase errors. Then, trajectory deviation estimation based on the weighted least square (WLS) method is performed to solve the 2-D SVME problem. Finally, the subaperture trajectory deviations are fused into a full-aperture trajectory deviation by an improved fusion strategy based on piecewise weighting. This approach is applied to real data from a new UAV-based UWB SAR. The results of both simulation and real data experiments are presented and verify the effectiveness of the proposed approach.