Security state estimation based on signal reconstruction for multi-vehicle systems under malicious attack

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.image