The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.
Fei XIONG
Army Engineering University of PLA
Hai WANG
Army Engineering University of PLA
Aijing LI
Army Engineering University of PLA
Dongping YU
Army Engineering University of PLA
Guodong WU
Army Engineering University of PLA
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Fei XIONG, Hai WANG, Aijing LI, Dongping YU, Guodong WU, "Compressed Sensing-Based Multi-Abnormality Self-Detecting and Faults Location Method for UAV Swarms" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 10, pp. 1975-1982, October 2019, doi: 10.1587/transcom.2018DRP0033.
Abstract: The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018DRP0033/_p
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@ARTICLE{e102-b_10_1975,
author={Fei XIONG, Hai WANG, Aijing LI, Dongping YU, Guodong WU, },
journal={IEICE TRANSACTIONS on Communications},
title={Compressed Sensing-Based Multi-Abnormality Self-Detecting and Faults Location Method for UAV Swarms},
year={2019},
volume={E102-B},
number={10},
pages={1975-1982},
abstract={The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.},
keywords={},
doi={10.1587/transcom.2018DRP0033},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - Compressed Sensing-Based Multi-Abnormality Self-Detecting and Faults Location Method for UAV Swarms
T2 - IEICE TRANSACTIONS on Communications
SP - 1975
EP - 1982
AU - Fei XIONG
AU - Hai WANG
AU - Aijing LI
AU - Dongping YU
AU - Guodong WU
PY - 2019
DO - 10.1587/transcom.2018DRP0033
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2019
AB - The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.
ER -