On the minimax robust Kalman Filter: A bounded estimation resources approach

This paper is devoted to a generalization of the non-standard Kalman Filter (KF) introduced in [4]. We deal with some restrictions of the technical resources in the context of a state estimation problem and study a constrained convex program. Moreover, we replace two main concepts of the conventional KF, namely, the fundamental Normality Hypothesis (NH) and the unconstrained optimization approach. The minimax methodology we propose make it possible to develop an effective quasi-explicit solution method for the practically motivated generalization of the Kalman-type filter. We present a rigorous formal analysis of the obtained algorithm. The resulting non-linear filter possesses a strong optimality properties. © 2017 IEEE.

• Fecha de publicación: 2018
• Institución: Universidad de Medellín
• Repositorio: Repositorio UDEM
• Idioma: eng
• OAI Identifier: oai:repository.udem.edu.co:11407/4884
• Acceso en línea: http://hdl.handle.net/11407/4884
• Palabra clave: Automation; Bandpass filters; Convex optimization; Process control; Estimation problem; Explicit solutions; Formal analysis; Non-linear filters; Robust Kalman filters; Standard Kalman filters; Technical resources; Unconstrained optimization; Kalman filters
• License: http://purl.org/coar/access_right/c_16ec