Implementation of Kalman Filter for Localization
Document Type
Book Chapter
Publication Date
9-6-2011
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
In general, an estimation algorithm predicts the values of quantities of interest from indirect, inaccurate, and uncertain observations. Numerous applications implement estimation algorithms. This chapter investigates the implementation of the linear and nonlinear Kalman filters (KFs) for localization problems. It first formulates the positioning problem in the estimation context. Next, the chapter presents a deterministic derivation for the KF. It also presents examples on the use of KF in localization. The chapter describes the extended Kalman filter (EKF) for nonlinear dynamic systems estimation, and presents an example on its implementation in positioning of a nonlinear system. The basic idea of the EKF is to linearize the nonlinear equations around the current estimate, and then apply a linear KF to the linearized model.
Publication Title
Handbook of Position Location: Theory, Practice, and Advances
Recommended Citation
Abdelkhalik, O.
(2011).
Implementation of Kalman Filter for Localization.
Handbook of Position Location: Theory, Practice, and Advances, 629-647.
http://doi.org/10.1002/9781118104750.ch19
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/3391