Recursive filter with partial knowledge on inputs and outputs

Jinya Su*, Baibing Li, Wen Hua Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)35-42
Number of pages8
JournalInternational Journal of Automation and Computing
Issue number1
Publication statusPublished - 11 Feb 2015

Bibliographical note

Publisher Copyright:
© 2015, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.


  • Bayesian inference
  • Kalman filter
  • missing measurements
  • state estimation
  • unknown inputs


Dive into the research topics of 'Recursive filter with partial knowledge on inputs and outputs'. Together they form a unique fingerprint.

Cite this