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Title: Recursive filter with partial knowledge on inputs and outputs
Authors: Su, Jinya
Li, Baibing
Chen, Wen-Hua
Keywords: Bayesian inference
Kalman filter
Missing measurements
State estimation
Unknown inputs
Issue Date: 2015
Publisher: © Springer International Publishing
Citation: 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.
Abstract: © 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.
Description: The final publication is available at Springer via http://dx.doi.org/10.1007/s11633-014-0864-8
Version: Accepted for publication
DOI: 10.1007/s11633-014-0864-8
URI: https://dspace.lboro.ac.uk/2134/21206
Publisher Link: http://dx.doi.org/10.1007/s11633-014-0864-8
ISSN: 1476-8186
Appears in Collections:Published Articles (Business)
Published Articles (Aeronautical and Automotive Engineering)

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