  A resilient state estimation scheme for uniformly observable nonlinear
systems, based on a method for local identification of sensor attacks, is
presented. The estimation problem is combinatorial in nature, and so many
methods require substantial computational and storage resources as the number
of sensors increases. To reduce the complexity, the proposed method performs
the attack identification with local subsets of the measurements, not with the
set of all measurements. A condition for nonlinear attack identification is
introduced as a relaxed version of existing redundant observability condition.
It is shown that an attack identification can be performed even when the state
cannot be recovered from the measurements. As a result, although a portion of
measurements are compromised, they can be locally identified and excluded from
the state estimation, and thus the true state can be recovered. Simulation
results demonstrate the effectiveness of the proposed scheme.
