Skip to main content
Log in

Conditions for stability of the extended Kalman filter and their application to the frequency tracking problem

  • Published:
Mathematics of Control, Signals and Systems Aims and scope Submit manuscript

Abstract

The error dynamics of the extended Kalman filter (EKF), employed as an observer for a general nonlinear, stochastic discrete time system, are analyzed. Sufficient conditions for the boundedness of the errors of the EKF are determined. An expression for the bound on the errors is given in terms of the size of the nonlinearities of the system and the error covariance matrices used in the design of the EKF. The results are applied to the design of a stable EKF frequency tracker for a signal with time-varying frequency.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. B. D. O. Anderson, R. R. Bitmead, C. R. Johnson, Jr, P. V. Kokotovic, R. L. Kosut, I. M. Y. Mareels, L. Praly, and B. D. Riedle.Stability of Adaptive Systems: Passivity and Averaging Analysis. M.I.T. Press, Cambridge, Massachusetts, 1986.

    Google Scholar 

  2. B. D. O. Anderson and J. B. Moore,Optimal Filtering. Prentice-Hall, Englewood Cliffs, New Jersey, 1979.

    Google Scholar 

  3. J. S. Baras, A. Bensoussan, and M. R. James. Dynamic observers as asymptotic limits of recursive filters: Special cases.SIAM J. Appl. Math.,48 (1988), 1147–1158.

    Google Scholar 

  4. S. W. Chan, G. C. Goodwin and K. S. Sin. Convergence properties of the Riccati difference equation in optimal filtering of nonstabilizable systems.IEEE Trans. Automat. Control,29 (1984), 110–118.

    Google Scholar 

  5. J. E. Dennis and R. B. Schnabel.Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, New Jersey, 1983.

    Google Scholar 

  6. J. J. Deyst, Jr and C. R. Price. Conditions for asymptotic stability of the discrete minimum-variance linear estimator.IEEE Trans. Automat. Control,13 (1968), 702–705.

    Google Scholar 

  7. B. James.Approaches to Multiharmonic Frequency Tracking and Estimation. Ph.D. thesis, Australian National University, Canberra, Australia, 1992.

    Google Scholar 

  8. C. N. Kelly and S. C. Gupta. The digital phase-locked loop as a near-optimum FM demodulator.IEEE Trans. Comm.,20 (1972), 406–411.

    Google Scholar 

  9. A. Nehorai and B. Porat. Adaptive comb filtering for harmonic signal enhancement.IEEE Trans. Acoustics, Speech Signal Processing,34 (1980), 1124–1138.

    Google Scholar 

  10. H. Nijmeijer. Observability of autonomous discrete time non-linear systems: A geometric approach.Internat. J. Control,36 (1982), 867–874.

    Google Scholar 

  11. P. J. Parker and B. D. O. Anderson. Frequency tracking of nonsinusoidal periodic signals in noise.Signal Processing,20 (1990), 127–152.

    Google Scholar 

  12. B. G. Quinn and J. M. Fernandes. A fast efficient technique for the estimation of frequency.Biometrika,78 (1991), 489–497.

    Google Scholar 

  13. D. L. Snyder.The State-Variable Approach to Continuous Estimation with Applications to Analog Communications Theory. M.I.T. Press, Boston, Massachusetts, 1969.

    Google Scholar 

  14. Y. Song and J. W. Grizzle. The extended Kalman filter as a local asymptotic observer for nonlinear discrete-time systems.J. Math. Systems, Estimation Control,5 (1995), 59–78.

    Google Scholar 

  15. R. L. Streit and R. F. Barrett. Frequency line tracking using Hidden Markov Models.IEEE Trans. Acoustics, Speech Signal Processing,38 (1990), 586–598.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

This research was supported by the Co-operative Research Centre for Robust and Adaptive Systems ((CR)2 ASys). The authors wish to acknowledge the funding of the activities of (CR)2 ASys by the Australian Commonwealth Government under the Co-operative Research Centre Program.

Rights and permissions

Reprints and permissions

About this article

Cite this article

La Scala, B.F., Bitmead, R.R. & James, M.R. Conditions for stability of the extended Kalman filter and their application to the frequency tracking problem. Math. Control Signal Systems 8, 1–26 (1995). https://doi.org/10.1007/BF01212364

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01212364

Key words

Navigation