Distributed State Estimation for Uncertain Linear Systems With a Recursive Architecture

• Published in: IEEE transactions on network science and engineering Vol. 9; no. 3; pp. 1163 - 1174
• Main Authors: Duan, Peihu, Lv, Yuezu, Duan, Zhisheng, Chen, Guanrong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Covariance; Covariance matrices; Discrete time systems; Distributed state estimation; Gaussian noise; Linear matrix inequalities; Linear systems; Measurement uncertainty; Parameters; Performance indices; Perturbation; Perturbation methods; Random noise; Recursive method; Robot sensing systems; Sensor network; Signal to noise ratio; State estimation; System uncertainty; Uncertainty
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2021.3134752

This paper investigates distributed state estimation for a class of discrete-time linear systems, with dynamics subject to parameter perturbation and Gaussian noise simultaneously. A sensor network is adopted, where each sensor estimates the system state by fusing its own measurements and its neighbors' information. A novel distributed state estimator is proposed, where the estimator gains are designed by using a state-error augmented technique. To check whether the estimator is effective on the infinite horizon, a very simple criterion only on system parameters is developed. Moreover, a relation between the estimation error covariance and the system state covariance is revealed, which shows that a performance index similar to the signal-to-noise ratio is ensured by the designed estimator. Compared with the existing methods in the literature, such as augmented methods and linear matrix inequalities-based methods, the estimation method proposed in this paper is much more computationally efficient. Finally, several numerical simulation results are demonstrated to illustrate the effectiveness of the new estimator.