Efficient autofocus of small multi-rotor UAV SAR by minimum entropy BP algorithm

Abstract

Small multi-rotor unmanned aerial vehicles (SMR-UAVs) are a promising platform for low-cost synthetic aperture radar (SAR) systems. However, SMR-UAVs usually suffer from serious position errors due to their unstable motion and low actuary position sensors, and autofocus is an indispensable step for their high-quality imaging. An efficient back-projection autofocus method is proposed for SMR-UAV SAR systems by the principle of minimum entropy. The position error estimation model via minimum entropy is derived. The conjugate-gradient method is used to efficiently estimate the position errors. Moreover, to improve the computing efficiency, the strong scatterer areas are estimated as the input of entropy estimation. The effectiveness of the algorithm is demonstrated using both simulation and experimental data.

Keywords

• entropy
• gradient methods
• synthetic aperture radar
• radar imaging
• autonomous aerial vehicles
• robot vision
• minimum entropy bp algorithm
• multirotor unmanned aerial vehicles
• low-cost synthetic aperture radar systems
• unstable motion
• low actuary position sensors
• high-quality imaging
• efficient back-projection autofocus method
• smr-uav sar systems
• position error estimation model
• conjugate-gradient method
• computing efficiency
• entropy estimation
• small multirotor uav sar
• minimum entropy principle
• scatterer areas