Event-Triggered State Observers for Sparse Sensor Noise/Attacks
This work addresses securing cyber-physical systems against malicious sensor attacks, presenting an incremental improvement with event-triggered methods.
The paper tackles the problem of reconstructing state from sensor measurements corrupted by sparse noise or attacks, proposing two algorithms: one for batch processing and another for incremental updates using event-triggered techniques to enhance computational performance.
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.