Recursive Optimization of State Estimation of Dynamic Processes in the Presence of Patchy Outliers in Observations

Article type: Research Article

Authors: Pupeikis, Rimantas

Affiliations: Institute of Mathematics and Informatics, Vilnius Gediminas Technical University, Akademijos 4, 2600 Vilnius, Lithuania. E-mail: [email protected]

Abstract: In the previous paper (Pupeikis, 1998), the problem of recursive estimation of the state of linear dynamic systems, described by an autoregressive model (AR), in the presence of time-varying outliers in observations to be processed has been considered. An approach to the robust recursive state estimation has been obtained and proved by estimating the real chemical process (Box and Jenkins, 1970). The aim of the given paper is the development of the abovementioned approach for the robust recursive state estimation of an autoregressive-moving average (ARMA) process in a case of additive noises with patchy outliers. The results of numerical simulation and the state estimation of the AR model (Figs. 1–4) and the real chemical process, described by the ARMA model, which is chosen from the same book of Box and Jenkins (Figs. 5–8) are given.

Keywords: dynamic system, Kalman filter, robustness, state estimation, optimization

DOI: 10.3233/INF-1999-10303

Journal: Informatica, vol. 10, no. 3, pp. 297-312, 1999