Event-Triggered State Observers for Sparse Sensor Noise/Attacks

By Yasser Shoukry, Paulo Tabuada. IEEE Transactions on Automatic Control, 2013

398 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a8af2ee999ef9eac9cfd9870fc8ee698cf5e7e34

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qing-Long Han
2. Xiaohua Ge
3. Xianming Zhang
4. Lei Ding
5. Derui Ding
6. Soummya Kar
7. Chen Peng
8. Dong Yue

In Control and Systems Engineering

1. Qing-Long Han
2. Xianming Zhang
3. Xiaohua Ge
4. Chen Peng
5. Lei Ding
6. Derui Ding
7. Dong Yue
8. Hongtao Sun

Citations in the corpus

• A Survey on Security Communication and Control for Smart Grids Under Malicious Cyber Attacks (2019, background): “Similar to [53], denote the set composed of the values of various sensors and actuators as S = {[S(·) k ]ii|i ∈ N, i ≤ m + p}.”
• Input-to-State Stabilizing Control for Cyber-Physical Systems With Multiple Transmission Channels Under Denial of Service (2018, background): “Referring to [23], the part dynamics of the UGV can be described as follows:”
• Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks (2018, background): “[9], the effect of sparse sensor attacks was considered to achieve a state reconstruction of discrete-time linear CPSs where an adversary arbitrarily falsified measurements of a subset of sensors.”
• Networked control systems: a survey of trends and techniques (2020, background): “now, various types of data integrity attacks have been identified in the control-theoretic literature, such as deception attacks [38], [117], [124]–[126]; false data injection attacks [127], [128]; replay attacks [20], [22], [129]; sparse sensor attacks [130].”
• Resilient Distributed Parameter Estimation With Heterogeneous Data (2018, background): “We will compare this sufficient condition against theoretical bounds of resilience established for centralized (or cloud) estimators, where a single processor has access to all of the measurements [23]–[25].”