Kalman Filter and Its Application

TLDR

Kalman filter provides minimum‑variance estimation for dynamic systems and has been extensively studied over the past three decades, especially for target‑tracking applications. This paper surveys recent developments of the Kalman filter and its variants, the Extended Kalman filter and Unscented Kalman filter. The authors introduce the basic theories of these filters and compare their merits and demerits. They conclude with key insights and future development trends for Kalman‑filtering techniques.

Abstract

Kalman filter is a minimum-variance estimation for dynamic systems and has attracted much attention with the increasing demands of target tracking. Various algorithms of Kalman filter was proposed for deriving optimal state estimation in the last thirty years. This paper briefly surveys the recent developments about Kalman filter (KF), Extended Kalman filter (EKF) and Unscented Kalman filter (UKF). The basic theories of Kalman filter are introduced, and the merits and demerits of them are analyzed and compared. Finally relevant conclusions and development trends are given.