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A resilient state estimation scheme for uniformly observable nonlinearsystems, based on a method for local identification of sensor attacks, ispresented. The estimation problem is combinatorial in nature, and so manymethods require substantial computational and storage resources as the numberof sensors increases. To reduce the complexity, the proposed method performsthe attack identification with local subsets of the measurements, not with theset of all measurements. A condition for nonlinear attack identification isintroduced as a relaxed version of existing redundant observability condition.It is shown that an attack identification can be performed even when the statecannot be recovered from the measurements. As a result, although a portion ofmeasurements are compromised, they can be locally identified and excluded fromthe state estimation, and thus the true state can be recovered. Simulationresults demonstrate the effectiveness of the proposed scheme.

Complexity reduction for resilient state estimation of uniformly observable nonlinear systems