Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering

Yifan Li; Jinwen Hu; Chunhui Zhao; Zhao Xu; Mingwei Lv; Wenzhe Wang

doi:10.1007/978-981-99-0479-2_14

Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering

Yifan Li, Jinwen Hu, Chunhui Zhao, Zhao Xu, Mingwei Lv, Wenzhe Wang

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

Abstract

This paper proposes a multi-sensor positioning technology for unmanned aerial vehicle (UAV) landing based on inertial navigation system (INS)/Global navigation satellite system (GNSS)/Radar integrated guidance system. In the harsh environment where sensor prior information is unreliable, measurement noise is non-stationary and measurement outliers are frequently generated, an adaptive federated filter based on variational Bayesian is used to achieve high accuracy and robustness of navigation system. Simulation results demonstrate that this guidance technology has a strong ability to adapt to non-stationary noise and frequent outliers, and the fusion accuracy is satisfactory.

Original language	English
Title of host publication	Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
Editors	Wenxing Fu, Mancang Gu, Yifeng Niu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	143-152
Number of pages	10
ISBN (Print)	9789819904785
DOIs	https://doi.org/10.1007/978-981-99-0479-2_14
State	Published - 2023
Event	International Conference on Autonomous Unmanned Systems, ICAUS 2022 - Xi'an, China Duration: 23 Sep 2022 → 25 Sep 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1010 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Autonomous Unmanned Systems, ICAUS 2022
Country/Territory	China
City	Xi'an
Period	23/09/22 → 25/09/22

Keywords

Federal Kalman filter
Information fusion
Variational bayesian

Access to Document

10.1007/978-981-99-0479-2_14

Cite this

Li, Y., Hu, J., Zhao, C., Xu, Z., Lv, M., & Wang, W. (2023). Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering. In W. Fu, M. Gu, & Y. Niu (Eds.), Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022 (pp. 143-152). (Lecture Notes in Electrical Engineering; Vol. 1010 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-0479-2_14

Li, Yifan ; Hu, Jinwen ; Zhao, Chunhui et al. / Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering. Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022. editor / Wenxing Fu ; Mancang Gu ; Yifeng Niu. Springer Science and Business Media Deutschland GmbH, 2023. pp. 143-152 (Lecture Notes in Electrical Engineering).

@inproceedings{578768d2f2024badb9b15b6dac7ad53a,

title = "Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering",

abstract = "This paper proposes a multi-sensor positioning technology for unmanned aerial vehicle (UAV) landing based on inertial navigation system (INS)/Global navigation satellite system (GNSS)/Radar integrated guidance system. In the harsh environment where sensor prior information is unreliable, measurement noise is non-stationary and measurement outliers are frequently generated, an adaptive federated filter based on variational Bayesian is used to achieve high accuracy and robustness of navigation system. Simulation results demonstrate that this guidance technology has a strong ability to adapt to non-stationary noise and frequent outliers, and the fusion accuracy is satisfactory.",

keywords = "Federal Kalman filter, Information fusion, Variational bayesian",

author = "Yifan Li and Jinwen Hu and Chunhui Zhao and Zhao Xu and Mingwei Lv and Wenzhe Wang",

note = "Publisher Copyright: {\textcopyright} 2023, Beijing HIWING Sci. and Tech. Info Inst.; International Conference on Autonomous Unmanned Systems, ICAUS 2022 ; Conference date: 23-09-2022 Through 25-09-2022",

year = "2023",

doi = "10.1007/978-981-99-0479-2_14",

language = "英语",

isbn = "9789819904785",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "143--152",

editor = "Wenxing Fu and Mancang Gu and Yifeng Niu",

booktitle = "Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022",

}

Li, Y, Hu, J , Zhao, C , Xu, Z, Lv, M & Wang, W 2023, Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering. in W Fu, M Gu & Y Niu (eds), Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022. Lecture Notes in Electrical Engineering, vol. 1010 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 143-152, International Conference on Autonomous Unmanned Systems, ICAUS 2022, Xi'an, China, 23/09/22. https://doi.org/10.1007/978-981-99-0479-2_14

Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering. / Li, Yifan; Hu, Jinwen ; Zhao, Chunhui et al.
Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022. ed. / Wenxing Fu; Mancang Gu; Yifeng Niu. Springer Science and Business Media Deutschland GmbH, 2023. p. 143-152 (Lecture Notes in Electrical Engineering; Vol. 1010 LNEE).

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

TY - GEN

T1 - Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering

AU - Li, Yifan

AU - Hu, Jinwen

AU - Zhao, Chunhui

AU - Xu, Zhao

AU - Lv, Mingwei

AU - Wang, Wenzhe

PY - 2023

Y1 - 2023

N2 - This paper proposes a multi-sensor positioning technology for unmanned aerial vehicle (UAV) landing based on inertial navigation system (INS)/Global navigation satellite system (GNSS)/Radar integrated guidance system. In the harsh environment where sensor prior information is unreliable, measurement noise is non-stationary and measurement outliers are frequently generated, an adaptive federated filter based on variational Bayesian is used to achieve high accuracy and robustness of navigation system. Simulation results demonstrate that this guidance technology has a strong ability to adapt to non-stationary noise and frequent outliers, and the fusion accuracy is satisfactory.

AB - This paper proposes a multi-sensor positioning technology for unmanned aerial vehicle (UAV) landing based on inertial navigation system (INS)/Global navigation satellite system (GNSS)/Radar integrated guidance system. In the harsh environment where sensor prior information is unreliable, measurement noise is non-stationary and measurement outliers are frequently generated, an adaptive federated filter based on variational Bayesian is used to achieve high accuracy and robustness of navigation system. Simulation results demonstrate that this guidance technology has a strong ability to adapt to non-stationary noise and frequent outliers, and the fusion accuracy is satisfactory.

KW - Federal Kalman filter

KW - Information fusion

KW - Variational bayesian

UR - http://www.scopus.com/inward/record.url?scp=85151052351&partnerID=8YFLogxK

U2 - 10.1007/978-981-99-0479-2_14

DO - 10.1007/978-981-99-0479-2_14

M3 - 会议稿件

AN - SCOPUS:85151052351

SN - 9789819904785

T3 - Lecture Notes in Electrical Engineering

SP - 143

EP - 152

BT - Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022

A2 - Fu, Wenxing

A2 - Gu, Mancang

A2 - Niu, Yifeng

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2022

Y2 - 23 September 2022 through 25 September 2022

ER -

Li Y, Hu J , Zhao C , Xu Z, Lv M, Wang W. Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering. In Fu W, Gu M, Niu Y, editors, Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 143-152. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-99-0479-2_14