Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models

This paper considers the problem of resiliency in the state estimation of a cyber-physical system when a portion of its sensor measurements contains malicious data added by an adversarial attacker. When the attack consists of arbitrary random uncorrelated data injection, compressive-sensing-based re...

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Veröffentlicht in:	IEEE transactions on industrial informatics 2020-01, Vol.16 (1), p.639-647
Hauptverfasser:	Anubi, Olugbenga Moses, Konstantinou, Charalambos
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Compressed sensing Compressive sensing Computer simulation Data models Distortion measurement false data injection attacks (FDIAs) Gaussian process (GP) Gaussian processes Mathematical models Optimization Regression resilient estimation State estimation
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