Linear minimum mean squared estimation of measurement bias driven by structured unknown inputs
- Author(s): Lin Zhou 1, 2 ; Yan Liang 1, 3 ; Jie Zhou 1, 3 ; Feng Yang 1, 3 ; Quan Pan 1, 3
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View affiliations
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Affiliations:
1:
School of Automation, Northwestern Polytechnical University, People's Republic of China;
2: School of Computer and Information Engineering, Henan University, Kaifeng, People's Republic of China;
3: Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an, People's Republic of China
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Affiliations:
1:
School of Automation, Northwestern Polytechnical University, People's Republic of China;
- Source:
Volume 8, Issue 8,
October 2014,
p.
977 – 986
DOI: 10.1049/iet-rsn.2013.0311 , Print ISSN 1751-8784, Online ISSN 1751-8792
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In this study, a generalised systematic bias (SB) is presented, which is represented via a dynamic model driven by structured unknown inputs (UI). The online SB estimation is implemented in two steps. In the first step, the state-free SB measurement and the UI-free SB dynamic model are derived in the case that UI-free condition holds. In the second step, the linear minimum mean squared filter is obtained via orthogonal principle, and the sufficient condition of filtering stability is presented. A simulation about target tracking is given to verify the proposed method.
Inspec keywords: filtering theory; mean square error methods
Other keywords: orthogonal principle; generalised systematic bias; structured unknown inputs; filtering stability; online SB estimation; UI-free SB dynamic model; linear minimum mean squared estimation; state-free SB measurement; mean squared filter
Subjects: Signal processing theory; Interpolation and function approximation (numerical analysis); Filtering methods in signal processing; Interpolation and function approximation (numerical analysis)
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