Target State Estimation and Prediction for High Speed Interception
This work addresses interception challenges for high-speed targets, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of accurately estimating and predicting the trajectory of high-speed targets for interception by using an extended Kalman filter with visual information and a motion model, and it is verified through simulation and hardware implementation.
Accurate estimation and prediction of trajectory is essential for interception of any high speed target. In this paper, an extended Kalman filter is used to estimate the current location of target from its visual information and then predict its future position by using the observation sequence. Target motion model is developed considering the approximate known pattern of the target trajectory. In this work, we utilise visual information of the target to carry out the predictions. The proposed algorithm is developed in ROS-Gazebo environment and is verified using hardware implementation.