Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

• Published in: IEEE transactions on cybernetics Vol. 52; no. 11; pp. 11504 - 11515
• Main Authors: Zou, Lei, Wang, Zidong, Dong, Hongli, Han, Qing-Long
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomaly detection; Constraints; Data analysis; Discrete time systems; Energy measurement; Energy-to-peak performance; Gain measurement; intermittent measurement outliers (IMOs); Noise measurement; Optimization; Outliers (statistics); parameter-dependent (PD) state estimator; Pollution measurement; Sequences; State estimation; Step functions; Technological innovation
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2021.3057545

This article is concerned with the energy-to-peak state estimation problem for a class of linear discrete-time systems with energy-bounded noises and intermittent measurement outliers (IMOs). In order to capture the intermittent nature, two sequences of step functions are introduced to model the occurrence of the IMOs. Furthermore, two special indices (i.e., minimum and maximum interval lengths) are adopted to describe the "occurrence frequency" of IMOs. Different from the considered energy-bounded noises, the outliers are assumed to have their magnitudes larger than certain thresholds. In order to achieve a satisfactory performance constraint on the energy-to-peak state estimation under the addressed kind of measurement outliers, a novel parameter-dependent (PD) state estimation strategy is developed to guarantee that the measurements contaminated by outliers would be removed in the estimation process. The proposed PD state estimation method is essentially a two-step process, where the first step is to examine the appearing and disappearing moments for each IMO by using a dedicatedly constructed outlier detection scheme, and the second step is to implement the state estimation task according to the outlier detection results. Sufficient conditions are obtained to ensure the existence of the desired estimator, and the gain matrix of the desired estimator is then derived by solving a constrained optimization problem. Finally, a simulation example is presented to illustrate the effectiveness of our developed PD state estimation strategy.