Target State Estimation and Prediction for High Speed Interception

TL;DR

This paper presents a method for high-speed target interception using an extended Kalman filter to estimate and predict target trajectories based on visual data, validated through simulation and hardware implementation.

Contribution

It introduces a novel target motion model and integrates visual information with Kalman filtering for accurate trajectory prediction in interception tasks.

Findings

Effective target position estimation using visual data.

Accurate future position prediction based on developed motion model.

Validated approach in ROS-Gazebo and hardware environments.

Abstract

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.