An improved unscented Kalman filter algorithm based on QR decomposition

Dongmei Li; Chengdong Xu; Xi Cai

doi:10.1007/978-3-319-13707-0_65

An improved unscented Kalman filter algorithm based on QR decomposition

Dongmei Li^*, Chengdong Xu, Xi Cai

^*Corresponding author for this work

School of Aerospace Engineering

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

The principle of extended Kalman filter (EKF), unscented Kalman filter (UKF) algorithm, and unscented transformation (UT) is studied. To simply the process of obtaining sigma points in UKF, an improved UKF algorithm is mentioned in this chapter. The theory formulation and computer simulation have been done for the comparison of these three nonlinear Kalman filters. Results show that all of them can accomplish the estimation task, but the improved UKF has higher accuracy and less computation cost than the other two.

Original language	English
Pages (from-to)	597-605
Number of pages	9
Journal	Lecture Notes in Electrical Engineering
Volume	334
DOIs	https://doi.org/10.1007/978-3-319-13707-0_65
Publication status	Published - 2015

Keywords

EK
QR decomposition
UKF

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10.1007/978-3-319-13707-0_65

Cite this

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title = "An improved unscented Kalman filter algorithm based on QR decomposition",

abstract = "The principle of extended Kalman filter (EKF), unscented Kalman filter (UKF) algorithm, and unscented transformation (UT) is studied. To simply the process of obtaining sigma points in UKF, an improved UKF algorithm is mentioned in this chapter. The theory formulation and computer simulation have been done for the comparison of these three nonlinear Kalman filters. Results show that all of them can accomplish the estimation task, but the improved UKF has higher accuracy and less computation cost than the other two.",

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AU - Xu, Chengdong

AU - Cai, Xi

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - The principle of extended Kalman filter (EKF), unscented Kalman filter (UKF) algorithm, and unscented transformation (UT) is studied. To simply the process of obtaining sigma points in UKF, an improved UKF algorithm is mentioned in this chapter. The theory formulation and computer simulation have been done for the comparison of these three nonlinear Kalman filters. Results show that all of them can accomplish the estimation task, but the improved UKF has higher accuracy and less computation cost than the other two.

AB - The principle of extended Kalman filter (EKF), unscented Kalman filter (UKF) algorithm, and unscented transformation (UT) is studied. To simply the process of obtaining sigma points in UKF, an improved UKF algorithm is mentioned in this chapter. The theory formulation and computer simulation have been done for the comparison of these three nonlinear Kalman filters. Results show that all of them can accomplish the estimation task, but the improved UKF has higher accuracy and less computation cost than the other two.

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KW - UKF

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VL - 334

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JO - Lecture Notes in Electrical Engineering

JF - Lecture Notes in Electrical Engineering

ER -

An improved unscented Kalman filter algorithm based on QR decomposition

Abstract

Keywords

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