Moving Object Tracking in 2D Using State Estimation

Abstract

This paper demonstrates the tracking of moving object in 2D using a state estimation algorithm. The proposed work covers the state estimation, its importance, applications, and different algorithms from which this paper mainly focuses on the Kalman filter algorithm and the Moving Horizon Estimation (MHE) algorithm. The explanation begins with the need for state estimation, types of the system models, and state estimation for deterministic and stochastic systems. There is a fundamental description of the Kalman filter algorithm and Moving Horizon Estimation algorithm. The paper describes the whole tracking process and also the implementation of a state estimation algorithm for object tracking using image process tools. The outcome of object tracking using the Kalman filter algorithm is presented using MATLAB software.KeywordsState estimationKalman filterMoving Horizon Estimation (MHE)Object tracking