IET Control Theory and Applications · 2020 · 13 citations · 27 references
ComputingComputer ScienceCybersecurityCyber-physical SystemsDistributed SystemsSensor FusionDistributed Sensor NetworksKalman FilteringSensor NetworksWireless Sensor Network SecurityWireless Sensor NetworksCovariance IntersectionDistributed EstimationDistributed FusionMulti-sensor FusionNumerical SimulationRandom Access ProtocolsUnscented Kalman FilterUnscented Kalman Filtering
This study investigates the distributed fusion estimation problem for multi‐sensor non‐linear networked systems with random access protocol (RAP) and cyber attacks. For each sensor, the RAP is applied to determine which channel can enter the communication networks at each transmission instant. The process of transmitting measurements to a corresponding local estimator (LE) via the wireless network may be attacked by a vicious attacker. Hence, a detector based on cross‐correlation of the causal sequence is proposed in the LE. Moreover, a distributed fusion estimation algorithm based on a sequential covariance intersection fusion technique is designed utilising local estimations calculated by modified unscented Kalman filter. A sufficient condition is obtained to guarantee the boundedness of fusion estimation error. Finally, a numerical simulation is given to demonstrate the effectiveness of the theoretical results.
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Sequential covariance intersection fusion Kalman filter
Zili Deng, Peng Zhang, Wenjuan Qi et al. · Information Sciences · 2011 · 261 citations
A multi-channel transmission schedule for remote state estimation under DoS attacks
Kemi Ding, Yuzhe Li, Daniel E. Quevedo et al. · Automatica · 2017 · 242 citations · Full text
Distributed filtering under false data injection attacks
Wen Yang, Yu Zhang, Guanrong Chen et al. · Automatica · 2019 · 198 citations