101. Rethinking the Mathematical Framework and Optimality of Set-Membership Filtering
- Author
-
Yirui Cong, Xiangke Wang, and Xiangyun Zhou
- Subjects
Mathematical optimization ,Computer science ,Mathematics::History and Overview ,Law of total probability ,Physics::Optics ,Systems and Control (eess.SY) ,Filter (signal processing) ,Electrical Engineering and Systems Science - Systems and Control ,Causal Markov condition ,Computer Science Applications ,Set (abstract data type) ,Range (mathematics) ,Bayes' theorem ,Control and Systems Engineering ,Bounded function ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Recursive Bayesian estimation - Abstract
Set-Membership Filter (SMF) has been extensively studied for state estimation in the presence of bounded noises with unknown statistics. Since it was first introduced in the 1960s, the studies on SMF have used the set-based description as its mathematical framework. One important issue that has been overlooked is the optimality of SMF. In this work, we put forward a new mathematical framework for SMF using concepts of uncertain variables. We first establish two basic properties of uncertain variables, namely, the law of total range (a non-stochastic version of the law of total probability) and the equivalent Bayes' rule. This enables us to put forward a general SMFing framework with established optimality. Furthermore, we obtain the optimal SMF under a non-stochastic Markov condition, which is shown to be fundamentally equivalent to the Bayes filter. Note that the classical SMF in the literature is only equivalent to the optimal SMF we obtained under the non-stochastic Markov condition. When this condition is violated, we show that the classical SMF is not optimal and it only gives an outer bound on the optimal estimation., 8 pages, 3 figures
- Published
- 2022