An Improved Map-Drift Algorithm for Unmanned Aerial Vehicle SAR Imaging

• Published in: IEEE geoscience and remote sensing letters Vol. 18; no. 11; pp. 1 - 5
• Main Authors: Huang, Yan, Liu, Feiyang, Chen, Zhanye, Li, Jie, Hong, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Apertures; Azimuth; Doppler effect; Drift; Electronics packaging; Error compensation; Imagery; Inertial navigation; Map-drift algorithm (MDA); motion compensation (MOCO); Movement; Navigation systems; Outliers (statistics); random sample consensus (RANSAC); SAR (radar); Synthetic aperture radar; Trajectory; unmanned aerial vehicle synthetic aperture radar (UAV SAR) imaging; Unmanned aerial vehicles
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3011973

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.