Skip to main content

Discrete-Time Filtering for Linear Systems in Correlated Noise with Non-Gaussian Initial Conditions: Formulas and Asymptotics

  • Chapter

Part of the book series: Progress in Systems and Control Theory ((PSCT,volume 4))

Abstract

We consider the one-step prediction problem for discrete-time linear systems in correlated plant and observation noises, and non-Gaussian initial conditions. Explicit representations are obtained for the MMSE and LMMSE (or Kalman) estimates of the state given past observations, as well as for the expected square of their difference. These formulae are obtained with the help of the Girsanov transformation for Gaussian white noise sequences, and display explicitly the dependence of the quantities of interest on the initial distribution. With the help of these formulae, we completely characterize the asymptotic behavior of the error sequence in the scalar time-invariant case.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. P. Caines and D. Mayne, “On the discrete-time Ricatti equation of optimal control,” Inter. J. of Control 12, pp. 785 - 794 (1970)

    Article  Google Scholar 

  2. P. Caines and D. Mayne, “On the discrete-time Ricatti equation of optimal control,” Inter. J. of Control 14, pp. 205 - 207 (1971).

    Article  Google Scholar 

  3. G. DiMasi and W. Runggaldier, “On measure transformations for combined filtering and parameter estimation in discrete time,” Systems E4 Control Letters 2, pp. 57 - 62 (1982).

    Article  Google Scholar 

  4. A. M. Makowski, “Filtering formulae for partially observed linear systems with non-Gaussian initial conditions,” Stochastics 16, pp. 1 - 24 (1986).

    Article  Google Scholar 

  5. R. B. Sowers, New Results in Discrete-Time Nonlinear Filtering, M.S. Thesis, Electrical Engineering Department, University of Maryland, College Park (MD), August 1988.

    Google Scholar 

  6. R.B. Sowers and A.M. Makowski, “Discrete-time filtering for linear systems in correlated noise with non-Gaussian initial conditions,” Proceedings of the Conference on Information Sciences and Systems, The Johns Hopkins University, Baltimore (MD ), March 1989.

    Google Scholar 

  7. R. B. Sowers and A.M. Makowski, “Filtering for discrete-time linear systems with non-Gaussian initial conditions: Asymptotic behavior of the difference between the MMSE and LMSE estimates,” submitted to IEEE Transactions on Automatic Control(1989).

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1990 Birkhäuser Boston

About this chapter

Cite this chapter

Sowers, R.B., Makowski, A.M. (1990). Discrete-Time Filtering for Linear Systems in Correlated Noise with Non-Gaussian Initial Conditions: Formulas and Asymptotics. In: Kaashoek, M.A., van Schuppen, J.H., Ran, A.C.M. (eds) Robust Control of Linear Systems and Nonlinear Control. Progress in Systems and Control Theory, vol 4. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-4484-4_39

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-4484-4_39

  • Publisher Name: Birkhäuser Boston

  • Print ISBN: 978-1-4612-8839-8

  • Online ISBN: 978-1-4612-4484-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics