Vision-based target motion estimation of multiple air vehicles using unscented information filter

Abstract

This paper presents target motion estimation for multiple air vehicles. The motion of the airborne target is estimated by vision information from camera sensors fixed to the followers. Each camera sensors provides information of the target by three angles: an azimuth angle, an elevation angle, and a subtended angle. In the frame work of information filter, each follower estimates position, velocity and size of the target while communicating own information with other vehicles. The information filter is suitable for decentralized multiple sensor estimation. Due to nonlinear characteristic of vision-based estimation problem, an unscented information filter is applied to target motion estimator. The unscented information filter provides the accuracy and robustness by embedding the unscented transform of the sigma point filter into the frame work of the information filter. The local filter propagates the target state and covariance through a statistical linear error propagation and updates by linear combination of the local information contribution terms. The simulation results demonstrate the performance of the proposed target motion estimator using the unscented information filtering of the three followers.