This paper describes vision-based target state estimation approaches for autonomous landing on a moving ground target. The state of moving ground target is estimated by using vision information from a gimbaled camera on an Unmanned Aerial Vehicle (UAV). Using the information from vision system, the UAV estimates the state of a moving target on the ground using the Unscented Kalman Filter (UKF). In this paper, three types of process model are compared by numerical simulations: state in inertial frame, state in inertial frame with position uncertainty of UAV, and relative state with acceleration of UAV.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.