An Introduction to Kalman Filtering Implementation for Localization and Tracking Applications

This chapter investigates the implementation of linear and nonlinear Kalman filters for localization, target tracking, and navigation. It formulates the positioning problem in the estimation context and presents a deterministic derivation for Kalman filters. The chapter introduces several types of Kalman filters used for localization, which include extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and constrained Kalman filter (CKF). Implementation examples for localization, target tracking, and navigation of these Kalman filters are offered, and their associated MATLAB codes are presented. In general, an estimation algorithm predicts the quantities of interest via direct or indirect observations. The chapter mainly presents the estimation algorithm for both target tracking and navigation applications. Such applications include vehicular navigation, aircraft tracking and navigation, satellite orbit and attitude determination, etc. Finally, the chapter offers relevant and more advanced topics to the reader.