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Security state estimation based on signal reconstruction for multi‐vehicle systems under malicious attack
by
Wang, Jing
, Wang, Siyuan
, Lu, Shan
, Zhou, Meng
in
Communication
/ control system security
/ Controllers
/ Data integration
/ Design
/ Error correction
/ Matched pursuit
/ multi‐robot systems
/ particle filtering (numerical methods)
/ Reconnaissance
/ Security
/ Sensors
/ Signal reconstruction
/ State estimation
/ Unmanned vehicles
/ Vehicles
2024
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Security state estimation based on signal reconstruction for multi‐vehicle systems under malicious attack
by
Wang, Jing
, Wang, Siyuan
, Lu, Shan
, Zhou, Meng
in
Communication
/ control system security
/ Controllers
/ Data integration
/ Design
/ Error correction
/ Matched pursuit
/ multi‐robot systems
/ particle filtering (numerical methods)
/ Reconnaissance
/ Security
/ Sensors
/ Signal reconstruction
/ State estimation
/ Unmanned vehicles
/ Vehicles
2024
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Do you wish to request the book?
Security state estimation based on signal reconstruction for multi‐vehicle systems under malicious attack
by
Wang, Jing
, Wang, Siyuan
, Lu, Shan
, Zhou, Meng
in
Communication
/ control system security
/ Controllers
/ Data integration
/ Design
/ Error correction
/ Matched pursuit
/ multi‐robot systems
/ particle filtering (numerical methods)
/ Reconnaissance
/ Security
/ Sensors
/ Signal reconstruction
/ State estimation
/ Unmanned vehicles
/ Vehicles
2024
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Security state estimation based on signal reconstruction for multi‐vehicle systems under malicious attack
Journal Article
Security state estimation based on signal reconstruction for multi‐vehicle systems under malicious attack
2024
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Overview
Aiming at the reconnaissance task of unmanned vehicle formation under the malicious attack, a security state estimation method based on attack signal reconstruction is proposed. First the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then the attack compensation based particle filter is designed to estimate the target state for each vehicle. An information fusion strategy is designed to obtain the final reconnaissance result based on agent centrality and the number of attacks on unmanned vehicles. Finally, simulations are provided to illustrate the effectiveness of the proposed method. Aiming at the reconnaissance task of unmanned vehicle formation under the malicious attack, a security state estimation method based on attack signal reconstruction is proposed. First, the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then, the attack compensation based particle filter is designed to estimate the target state for each vehicle. An information fusion strategy is designed to obtain the final reconnaissance result based on node centrality and the number of attacks on unmanned vehicles. Finally, simulations are provided to illustrate the effectiveness of the proposed method.
Publisher
John Wiley & Sons, Inc,Wiley
Subject
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