SAR Mocomp by machine Learning


  • Brianna Christensen Azusa Pacific University
  • Enson Chang Azusa Pacific University
  • Nathaniel Tamminga Azusa Pacific University



Motion Compensation, Synthetic Aperture Radar, Convolutional Neural Network, Unmanned Aerial Vehicle


All unmanned aerial vehicles that use synthetic aperture radar (SAR) systems are equipped with inertial navigation systems (INS) to reduce motion error. Additional motion compensation (MOCOMP) from the data itself is still necessary to achieve required accuracy of a SAR. An affordable method for small drones has yet to be created. We propose machine learning with deep convolutional neural network (CNN) to extract motion error such as sway (right and left) and surge (forward). Results show that the CNN is capable of recognizing gradual drone motion deviations. It has the potential to pick up sudden motion error as well, overcoming major deficiencies of traditional MOCOMP methods, and the need for INS.

Author Biography

Enson Chang, Azusa Pacific University

Associate Professor, Department of Mathematics, Physics, and Statistics

Affiliated Faculty, Department of Engineering and Computer Science




How to Cite

Christensen, B., Chang, E., & Tamminga, N. (2021). SAR Mocomp by machine Learning. Virginia Journal of Business, Technology, and Science, 1(1).