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Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks
2019
Sensors
In this paper, a cluster-based approach is used to address the distributed fusion estimation problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of random deception attacks. At each sampling time, measured outputs of the signal are provided by a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local processor which gathers the measured outputs of its sensors and, in turn, the local processors of all clusters
doi:10.3390/s19143112
fatcat:3jmrbw66zzgcfn5us3offgvvny