Filtering for systems subject to unknown inputs without a priori initial information. (October 2020)

• Record Type: Journal Article
• Authors: Kong, He; Shan, Mao; Su, Daobilige; Qiao, Yongliang; Al-Azzawi, Abdullah; Sukkarieh, Salah
• Is Part Of: Automatica. Volume 120(2020)
• Publication Date: 2020-10
• Subjects: Estimation -- Arbitrary unknown input -- Kalman filter -- Internal model principle; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2020.109122
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: The last few decades have witnessed much development in filtering of systems with Gaussian noises and arbitrary unknown inputs. Nonetheless, there are still some important design questions that warrant thorough discussions. Especially, the existing literature has shown that for unbiased and minimum variance estimation of the state and the unknown input, the initial guess of the state has to be unbiased. This clearly raises the question of whether and under what conditions one can design an unbiased and minimum variance filter, without making such a stringent assumption. The above-mentioned question will be investigated systematically in this paper, i.e., design of the filter is sought to be independent of a priori information about the initial conditions. In particular, for both cases with and without direct feedthrough, we establish necessary and sufficient conditions for unbiased and minimum variance estimation of the state/unknown input, independently of a priori initial conditions, respectively. When the former conditions do not hold, we carry out a thorough analysis of all possible scenarios. For each scenario, we present detailed discussions regarding whether and what can be achieved in terms of unbiased estimation, independently of a priori initial conditions. Extensions to the case with time-delays, conceptually like Kalman smoothing where future measurements are allowed in estimation, will also be presented, amongst others.