Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine

Yong Duan; Yuanpeng Zhao; Jingyue Pang; Liansheng Liu; Datong Liu

doi:10.1109/SDPC.2017.125

Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine

Yong Duan
, Yuanpeng Zhao
, Jingyue Pang
, Liansheng Liu
, Datong Liu

School of Electronics and Information Engineering

Xi'an ASN Technology Group Co. Ltd
Harbin Institute of Technology

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

6 Scopus citations

Abstract

In recent years, Unmanned Aerial Vehicle (UAV) has been gaining more and more attention for military and civilian utilization. How to monitor its condition is a crucial problem. The accuracy of the sensing data is one of the basic requirements to achieve its correct condition. Thus, one kind of data anomaly detection approaches by Relevance Vector machine (RVM) is proposed in this article. By utilizing the visual UAV simulator FlgihtGear, the sensing data with anomalous data are generated. The effectiveness of the proposed approach for detecting the anomalous data contained in the sensing data is evaluated.

Original language	English
Title of host publication	Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
Editors	Wei Guo, Jose Valente de Oliveira, Chuan Li, Yun Bai, Ping Ding, Juanjuan Shi
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	638-641
Number of pages	4
ISBN (Electronic)	9781509040209
DOIs	https://doi.org/10.1109/SDPC.2017.125
State	Published - 9 Dec 2017
Event	2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 - Shanghai, China Duration: 16 Aug 2017 → 18 Aug 2017

Publication series

Name	Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
Volume	2017-December

Conference

Conference	2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
Country/Territory	China
City	Shanghai
Period	16/08/17 → 18/08/17

Keywords

Relevance Vector Machine
Unmanned Aerial Vehicle
data anomaly detection

Access to Document

10.1109/SDPC.2017.125

Cite this

Duan, Y., Zhao, Y., Pang, J., Liu, L., & Liu, D. (2017). Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine. In W. Guo, J. V. de Oliveira, C. Li, Y. Bai, P. Ding, & J. Shi (Eds.), Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 (pp. 638-641). (Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SDPC.2017.125

Duan, Yong ; Zhao, Yuanpeng ; Pang, Jingyue et al. / Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine. Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017. editor / Wei Guo ; Jose Valente de Oliveira ; Chuan Li ; Yun Bai ; Ping Ding ; Juanjuan Shi. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 638-641 (Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017).

@inproceedings{bb5a215a916b4f0d85a8f64daed22e93,

title = "Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine",

abstract = "In recent years, Unmanned Aerial Vehicle (UAV) has been gaining more and more attention for military and civilian utilization. How to monitor its condition is a crucial problem. The accuracy of the sensing data is one of the basic requirements to achieve its correct condition. Thus, one kind of data anomaly detection approaches by Relevance Vector machine (RVM) is proposed in this article. By utilizing the visual UAV simulator FlgihtGear, the sensing data with anomalous data are generated. The effectiveness of the proposed approach for detecting the anomalous data contained in the sensing data is evaluated.",

keywords = "Relevance Vector Machine, Unmanned Aerial Vehicle, data anomaly detection",

author = "Yong Duan and Yuanpeng Zhao and Jingyue Pang and Liansheng Liu and Datong Liu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 ; Conference date: 16-08-2017 Through 18-08-2017",

year = "2017",

month = dec,

day = "9",

doi = "10.1109/SDPC.2017.125",

language = "英语",

series = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",

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

pages = "638--641",

editor = "Wei Guo and \{de Oliveira\}, \{Jose Valente\} and Chuan Li and Yun Bai and Ping Ding and Juanjuan Shi",

booktitle = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",

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Duan, Y, Zhao, Y, Pang, J, Liu, L & Liu, D 2017, Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine. in W Guo, JV de Oliveira, C Li, Y Bai, P Ding & J Shi (eds), Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017. Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 638-641, 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, Shanghai, China, 16/08/17. https://doi.org/10.1109/SDPC.2017.125

Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine. / Duan, Yong; Zhao, Yuanpeng; Pang, Jingyue et al.
Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017. ed. / Wei Guo; Jose Valente de Oliveira; Chuan Li; Yun Bai; Ping Ding; Juanjuan Shi. Institute of Electrical and Electronics Engineers Inc., 2017. p. 638-641 (Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017; Vol. 2017-December).

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

TY - GEN

T1 - Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine

AU - Duan, Yong

AU - Zhao, Yuanpeng

AU - Pang, Jingyue

AU - Liu, Liansheng

AU - Liu, Datong

PY - 2017/12/9

Y1 - 2017/12/9

N2 - In recent years, Unmanned Aerial Vehicle (UAV) has been gaining more and more attention for military and civilian utilization. How to monitor its condition is a crucial problem. The accuracy of the sensing data is one of the basic requirements to achieve its correct condition. Thus, one kind of data anomaly detection approaches by Relevance Vector machine (RVM) is proposed in this article. By utilizing the visual UAV simulator FlgihtGear, the sensing data with anomalous data are generated. The effectiveness of the proposed approach for detecting the anomalous data contained in the sensing data is evaluated.

AB - In recent years, Unmanned Aerial Vehicle (UAV) has been gaining more and more attention for military and civilian utilization. How to monitor its condition is a crucial problem. The accuracy of the sensing data is one of the basic requirements to achieve its correct condition. Thus, one kind of data anomaly detection approaches by Relevance Vector machine (RVM) is proposed in this article. By utilizing the visual UAV simulator FlgihtGear, the sensing data with anomalous data are generated. The effectiveness of the proposed approach for detecting the anomalous data contained in the sensing data is evaluated.

KW - Relevance Vector Machine

KW - Unmanned Aerial Vehicle

KW - data anomaly detection

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DO - 10.1109/SDPC.2017.125

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SP - 638

EP - 641

BT - Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017

A2 - Guo, Wei

A2 - de Oliveira, Jose Valente

A2 - Li, Chuan

A2 - Bai, Yun

A2 - Ding, Ping

A2 - Shi, Juanjuan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017

Y2 - 16 August 2017 through 18 August 2017

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Duan Y, Zhao Y, Pang J, Liu L , Liu D. Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine. In Guo W, de Oliveira JV, Li C, Bai Y, Ding P, Shi J, editors, Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 638-641. (Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017). doi: 10.1109/SDPC.2017.125

Unmanned Aerial Vehicle Sensing Data Anomaly Detection by Relevance Vector Machine

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Keywords

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