A Novel Autofocus Framework for UAV SAR Imagery: Motion Error Extraction From Symmetric Triangular FMCW Differential Signal

Synthetic aperture radar (SAR) installed on unmanned aerial vehicles (UAVs) has drawn increasing attention in the area of remote sensing due to its high spatial resolution and low energy consumption. However, due to the sensitivity toward atmospheric turbulences, strong motion errors can defocus the image in both azimuth and range directions. In this article, based on symmetric triangular linear frequency-modulation continuous microwave (STLFMCW) signals, a novel motion error estimation method is proposed. The core concept is to eliminate the effect of residual range cell migration (RCM) and investigate the motion parameters from azimuth phase history by range-frequency-domain interferometry between the up-ramp and down-ramp chirp sections. With preknowledge of the structural characteristics of the differential signal, we first obtain the high-order component, then estimate the quadratic term with an azimuth profile width minimization (APWM) algorithm on the basis of bisection search, and subsequently remove the linear part through phase gradient matching and joining. Consequently, the desired motion errors can be achieved purely based on raw data. Simulations demonstrate that our proposed algorithm can achieve high accuracy with or without standard prominent point targets. Experimental results on a W-band UAV SAR system in strip-map mode indicate that this approach also outperforms other existing postprocessing autofocus methods.