Kalman filter-based identification for systems with randomly missing measurements in a network environment

• Published in: International journal of control Vol. 83; no. 3; pp. 538 - 551
• Main Authors: Shi, Yang, Fang, Huazhen
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.03.2010; Taylor & Francis; Taylor & Francis Ltd
• Subjects: Applied sciences; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Kalman filter; Modelling and identification; networked control systems; Parameter estimation; randomly missing data; Studies; system identification; Parameter estimation; Probabilistic approach; Kalman filter; Transmission protocol; Output signal; Delay system; Recursive algorithm; Missing data; Observer; System identification; Transmission loss; Input output model; Recursive estimation
• ISSN: 0020-7179; 1366-5820
• DOI: 10.1080/00207170903273987

We consider the problem of parameter estimation and output estimation for systems in a transmission control protocol (TCP) based network environment. As a result of networked-induced time delays and packet loss, the input and output data are inevitably subject to randomly missing data. Based on the available incomplete data, we first model the input and output missing data as two separate Bernoulli processes characterised by probabilities of missing data, then a missing output estimator is designed, and finally we develop a recursive algorithm for parameter estimation by modifying the Kalman filter-based algorithm. Under the stochastic framework, convergence properties of both the parameter estimation and output estimation are established. Simulation results illustrate the effectiveness of the proposed algorithms.