  This paper describes two algorithms for state reconstruction from sensor
measurements that are corrupted with sparse, but otherwise arbitrary, "noise".
These results are motivated by the need to secure cyber-physical systems
against a malicious adversary that can arbitrarily corrupt sensor measurements.
The first algorithm reconstructs the state from a batch of sensor measurements
while the second algorithm is able to incorporate new measurements as they
become available, in the spirit of a Luenberger observer. A distinguishing
point of these algorithms is the use of event-triggered techniques to improve
the computational performance of the proposed algorithms.
