IJAC-2013-12-235.pdf (592.54 kB)
Recursive filter with partial knowledge on inputs and outputs
journal contribution
posted on 2016-05-12, 13:39 authored by Jinya Su, Baibing LiBaibing Li, Wen-Hua ChenWen-Hua Chen© 2015, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg. This paper investigates the problem of state estimation for discrete-time stochastic linear systems, where additional knowledge on the unknown inputs is available at an aggregate level and the knowledge on the missing measurements can be described by a known stochastic distribution. Firstly, the available knowledge on the unknown inputs and the state equation is used to form the prior distribution of the state vector at each time step. Secondly, to obtain an analytically tractable likelihood function, the effect of missing measurements is broken down into a systematic part and a random part, and the latter is modeled as part of the observation noise. Then, a recursive filter is obtained based on Bayesian inference. Finally, a numerical example is provided to evaluate the performance of the proposed methods.
History
School
- Business and Economics
Department
- Business
Published in
International Journal of Automation and ComputingVolume
12Issue
1Pages
35 - 42Citation
SU, J., LI, B. and CHEN, W-H., 2015. Recursive filter with partial knowledge on inputs and outputs. International Journal of Automation and Computing, 12(1), pp. 35-42.Publisher
© Springer International PublishingVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2015Notes
The final publication is available at Springer via http://dx.doi.org/10.1007/s11633-014-0864-8ISSN
1476-8186eISSN
1751-8520Publisher version
Language
- en