Application of the Unscented Kalman Filter for Tracking a Maneuvering Tank Modeled with a Second-Order Gauss-Markov Process: A Comparative Analysis with the Extended Kalman Filter

Abstract

This paper presents the application of the unscented Kalman filter (UKF) for estimating the dynamic states of a maneuvering tank using a second-order Gauss-Markov process model. The proposed method is effective in capturing the oscillatory characteristics, damping effects, and the impact of uncertain disturbances on the tank’s dynamics, leading to improved estimation accuracy compared to traditional linear methods. Simulation results demonstrate that the UKF outperforms the extended Kalman filter (EKF) in accurately estimating the tank’s position, velocity, and acceleration, even in the presence of significant noise and disturbances. This study highlights the superiority of the UKF in handling nonlinear dynamics and its potential application in military vehicle tracking systems.