A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal

He Kanghui; Dong Chaoyang

doi:10.1109/ICPICS47731.2019.8942402

A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal

He Kanghui
, Dong Chaoyang

School of Aeronautic Science and Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

For the UAV inertial/GPS integrated navigation system, considering the problem of GPS data interruption in navigation process, this technical paper designs an improved sensors fusion algorithm. Combining the traditional extended Kalman filter (EKF) technology with strong tracking filter, a fuzzy strong tracking extended Kalman filter algorithm is designed by using the membership function of the fuzzy theory. Then the navigation simulation model of UAV is established. The simulation results show that the improved algorithm can quickly adapt to the sudden change of GPS signal, that is, when the GPS signal restores from the fault state to the normal state, the improved algorithm can converge to the stable state more quickly than the EKF algorithm, and complete the estimation of flight state again. At the same time, compared with EKF and strong tracking extended Kalman filter (SKEKF), the improved algorithm in this paper has higher estimation accuracy.

Original language	English
Title of host publication	2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	254-259
Number of pages	6
ISBN (Electronic)	9781728137209
DOIs	https://doi.org/10.1109/ICPICS47731.2019.8942402
State	Published - Jul 2019
Event	2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019 - Shenyang, China Duration: 12 Jul 2019 → 14 Jul 2019

Publication series

Name	2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019

Conference

Conference	2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019
Country/Territory	China
City	Shenyang
Period	12/07/19 → 14/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

extended kalman filter
integrated navigation
membership function
strong trracking

Access to Document

10.1109/ICPICS47731.2019.8942402

Cite this

Kanghui, H., & Chaoyang, D. (2019). A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal. In 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019 (pp. 254-259). Article 8942402 (2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPICS47731.2019.8942402

Kanghui, He ; Chaoyang, Dong. / A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal. 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 254-259 (2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019).

@inproceedings{85ed079293534bcda3ce4ef960fc107f,

title = "A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal",

abstract = "For the UAV inertial/GPS integrated navigation system, considering the problem of GPS data interruption in navigation process, this technical paper designs an improved sensors fusion algorithm. Combining the traditional extended Kalman filter (EKF) technology with strong tracking filter, a fuzzy strong tracking extended Kalman filter algorithm is designed by using the membership function of the fuzzy theory. Then the navigation simulation model of UAV is established. The simulation results show that the improved algorithm can quickly adapt to the sudden change of GPS signal, that is, when the GPS signal restores from the fault state to the normal state, the improved algorithm can converge to the stable state more quickly than the EKF algorithm, and complete the estimation of flight state again. At the same time, compared with EKF and strong tracking extended Kalman filter (SKEKF), the improved algorithm in this paper has higher estimation accuracy.",

keywords = "extended kalman filter, integrated navigation, membership function, strong trracking",

author = "He Kanghui and Dong Chaoyang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019 ; Conference date: 12-07-2019 Through 14-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ICPICS47731.2019.8942402",

language = "英语",

series = "2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "254--259",

booktitle = "2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019",

address = "美国",

}

Kanghui, H & Chaoyang, D 2019, A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal. in 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019., 8942402, 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 254-259, 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019, Shenyang, China, 12/07/19. https://doi.org/10.1109/ICPICS47731.2019.8942402

A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal. / Kanghui, He; Chaoyang, Dong.
2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 254-259 8942402 (2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal

AU - Kanghui, He

AU - Chaoyang, Dong

PY - 2019/7

Y1 - 2019/7

N2 - For the UAV inertial/GPS integrated navigation system, considering the problem of GPS data interruption in navigation process, this technical paper designs an improved sensors fusion algorithm. Combining the traditional extended Kalman filter (EKF) technology with strong tracking filter, a fuzzy strong tracking extended Kalman filter algorithm is designed by using the membership function of the fuzzy theory. Then the navigation simulation model of UAV is established. The simulation results show that the improved algorithm can quickly adapt to the sudden change of GPS signal, that is, when the GPS signal restores from the fault state to the normal state, the improved algorithm can converge to the stable state more quickly than the EKF algorithm, and complete the estimation of flight state again. At the same time, compared with EKF and strong tracking extended Kalman filter (SKEKF), the improved algorithm in this paper has higher estimation accuracy.

AB - For the UAV inertial/GPS integrated navigation system, considering the problem of GPS data interruption in navigation process, this technical paper designs an improved sensors fusion algorithm. Combining the traditional extended Kalman filter (EKF) technology with strong tracking filter, a fuzzy strong tracking extended Kalman filter algorithm is designed by using the membership function of the fuzzy theory. Then the navigation simulation model of UAV is established. The simulation results show that the improved algorithm can quickly adapt to the sudden change of GPS signal, that is, when the GPS signal restores from the fault state to the normal state, the improved algorithm can converge to the stable state more quickly than the EKF algorithm, and complete the estimation of flight state again. At the same time, compared with EKF and strong tracking extended Kalman filter (SKEKF), the improved algorithm in this paper has higher estimation accuracy.

KW - extended kalman filter

KW - integrated navigation

KW - membership function

KW - strong trracking

UR - https://www.scopus.com/pages/publications/85077968866

U2 - 10.1109/ICPICS47731.2019.8942402

DO - 10.1109/ICPICS47731.2019.8942402

M3 - 会议稿件

AN - SCOPUS:85077968866

T3 - 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019

SP - 254

EP - 259

BT - 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019

Y2 - 12 July 2019 through 14 July 2019

ER -

Kanghui H, Chaoyang D. A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal. In 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 254-259. 8942402. (2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019). doi: 10.1109/ICPICS47731.2019.8942402

A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal

Abstract

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UN SDGs

Keywords

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