Application of EKF and UKF in Target Tracking Problem
Shaoke Yang, Hongxin Li · 2016
There are many approaches to estimate the state of target tracking which plays an important role in the area of early warning and detecting system. Linear and Nonlinear are the two major types of state estimation processes. The famous Kalman filter (KF) which is rooted in the state-space formulation of liner dynamical system provides a recursive solution to the linear problem. The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are derived from the KF. The EKF is the nonlinear version of the KF which linearizes about the mean and covariance, while the UKF is best known nonlinear estimates. This paper gives an approach to analyze the difference between EKF and UKF in state estimation, and because of the target tracking problem contains many nonlinear variable, an implementation of UKF is presented in the last section.