Skip to main content
Log in

A general approach to constructing parameter identification algorithms in the class of square root filters with orthogonal and J-orthogonal tranformations

  • Stochastic Systems, Queueing Systems
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

We study modern implementations of the discrete Kalman filter, namely array square-root algorithms. An important feature of such algorithms is the use of orthogonal and J-orthogonal transformations on each filtering step. For the first time, we develop for this class of algorithms a simple universal approach that lets us generalize any numerically stable implementation of this type to the case of updates in sensitivity equations of the filter with respect to unknown system model parameters. An advantage of the resulting adaptive schemes is their numerical stability with respect to machine rounding errors. Estimation of the noisy state vector of the system and identification of unknown system parameters occur simultaneously. The proposed approach can be used for parameter identification problems, adaptive control problems, experiment planning, and others.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Khazen, E.M., Metody optimal’nykh statisticheskikh reshenii i zadachi optimal’nogo upravleniya (Methods of Optimal Statistical Decisions, Optimal Control, and Stochastic Differential Equations), Moscow: Sovetskoe Radio, 1968.

    Google Scholar 

  2. Ogarkov, M.A., Metody statisticheskogo otsenivaniya parametrov sluchainykh protsessov (Statistic Estimation Methods for Parameters of Random Processes), Moscow: Energoatomizdat, 1990.

    Google Scholar 

  3. Ljung, L., System Identification: Theory for the User, Englewood Cliffs: Prentice Hall, 1987. Translated under the title Identifikatsiya sistem. Teoriya dlya pol’zovatelya, Moscow: Nauka, 1991.

    MATH  Google Scholar 

  4. Aström, K.J., Maximum Likelihood and Prediction Error Methods, Automatica, 1980, vol. 16, pp. 551–574.

    Article  MATH  Google Scholar 

  5. Gupta, N.K. and Mehra, R.K., Computational Aspects of Maximum Likelihood Estimation and Reduction in Sensitivity Function Calculations, IEEE Trans. Automat. Control, 1974, vol. AC-19, no. 6, pp. 774–783.

    Article  MathSciNet  Google Scholar 

  6. Mehra, R.K., Optimal Input Signals for Parameter Estimation in Dynamic Systems—Survey and New Results, IEEE Trans. Automat. Control, 1974, vol. AC-19, no. 6, pp. 753–768.

    Article  MathSciNet  Google Scholar 

  7. Denisov, V.I., Chubich, V.M., and Chernikova, O.S., Active Parametric Identification of Stochastic Linear Discrete Systems in the Time Domain, Sib. J. Ind. Math., 2003, vol. 6, no. 3, pp. 70–87.

    MATH  MathSciNet  Google Scholar 

  8. Verhaegen, M. and Van Dooren, P., Numerical Aspects of Different Kalman Filter Implementations, IEEE Trans. Automat. Control, 1986, vol. AC-31, no. 10, pp. 907–917.

    Article  Google Scholar 

  9. Dyer, P. and McReynolds, S., Extension of Square-Root Filtering to Include Process Noise, J. Optim. Theory Appl., 1969, no. 3, pp. 444–459.

    Google Scholar 

  10. Kaminski, P.G., Bryson, A.E., and Schmidt, S.F., Discrete Square-Root Filtering: A Survey of Current Techniques, IEEE Trans. Automat. Control, 1971, vol. AC-16, no. 6, pp. 727–735.

    Article  Google Scholar 

  11. Bierman, G.J., Factorization Methods for Discrete Sequential Estimation, New York: Academic, 1977.

    MATH  Google Scholar 

  12. Park, P. and Kailath, T., New Square-Root Algorithms for Kalman Filtering, IEEE Trans. Automat. Control, 1995, vol. 40, no. 5, pp. 895–899.

    Article  MATH  MathSciNet  Google Scholar 

  13. Kailath, T., Sayed, A.H., and Hassibi, B., Linear Estimation, New Jersey: Prentice Hall, 2000.

    Google Scholar 

  14. Kailath, T., Some New Algorithms for Recursive Estimation in Constant Linear Systems, IEEE Trans. Inform. Theory, 1973, vol. IT-19, no. 11, pp. 750–760.

    Article  MathSciNet  Google Scholar 

  15. Morf, M., Sidhu, G.S., and Kailath, T., Some New Algorithms for Recursive Estimation in Constant, Linear Discrete-Time Systems, IEEE Trans. Automat. Control, 1974, vol. AC-19, no. 4, pp. 315–323.

    Article  Google Scholar 

  16. Morf, M. and Kailath, T., Square-Root Algorithms for Least-Squares Estimation, IEEE Trans. Automat. Control, 1975, vol. AC-20, no. 4, pp. 487–497.

    Article  MathSciNet  Google Scholar 

  17. Sayed, A.H. and Kailath, T., Extended Chandrasekhar Recursions, IEEE Trans. Automat. Control, 1994, vol. 39, no. 3, pp. 619–622.

    Article  MATH  MathSciNet  Google Scholar 

  18. Gibbs, R.G., Square Root Modified Bryson-Frazier Smoother, IEEE Trans. Automat. Control, 2011, vol. 56, no. 2, pp. 425–456.

    Article  MathSciNet  Google Scholar 

  19. Hassibi, B. and Sayed, A.H., Array Algorithms for H Estimation, IEEE Trans. Automat. Control, 2000, vol. 45, no. 4, pp. 702–706.

    Article  MATH  MathSciNet  Google Scholar 

  20. Bierman, G.J., Belzer, M.R., Vandergraft, J.S., and Porter, D.W., Maximum Likelihood Estimation Using Square Root Information Filters, IEEE Trans. Automat. Control, 1990, vol. 35, no. 12, pp. 1293–1299.

    Article  MATH  MathSciNet  Google Scholar 

  21. Kulikova, M.V., Likelihood Gradient Evaluation Using Square-Root Covariance Filters, IEEE Trans. Automat. Control, 2009, vol. 54, no. 3, pp. 646–651.

    Article  MathSciNet  Google Scholar 

  22. Kulikova, M.V., Maximum Likelihood Estimation of Linear Stochastic Systems in the Class of Sequential Square-Root Orthogonal Filtering Methods, Autom. Remote Control, 2011, vol. 72, no. 4, pp. 766–786.

    Article  MATH  MathSciNet  Google Scholar 

  23. Tsyganova, Yu.V., Computing the Gradient of the Auxiliary Quality Functional in the Parametric Identification Problem for Stochastic Systems, Autom. Remote Control, 2011, vol. 72, no. 9, pp. 1925–1940.

    Article  MATH  MathSciNet  Google Scholar 

  24. Tsyganova, Yu.V. and Kulikova, M.V., On Efficient Parametric Identification Methods for Linear Discrete Stochastic Systems, Autom. Remote Control, 2012, vol. 73, no. 6, pp. 962–975.

    Article  Google Scholar 

  25. Grewal, M.S. and Andrews, A.P., Kalman Filtering: Theory and Practice, New Jersey: Prentice Hall, 2001.

    Google Scholar 

  26. Higham, N.J., Accuracy and Stability of Numerical Algorithms, Philadelphia: SIAM, 2002, 2nd ed.

    Book  MATH  Google Scholar 

  27. Higham, N.J., J-Orthogonal Matrices: Properties and Generalization, SIAM Rev., 2003, vol. 45, no. 3, pp. 504–519.

    Article  MATH  MathSciNet  Google Scholar 

  28. Gantmaher, F.R., Teoriya matrits (Theory of Matrices), Moscow: Nauka, 1988, 4th ed.

    Google Scholar 

  29. Kulikova, M.V. and Pacheco, A., Kalman Filter Sensitivity Evaluation with Orthogonal and J-Orthogonal Transformations, IEEE Trans. Automat. Control, 2013, vol. 58, no. 7. pp. 1798–1804.

    Article  MathSciNet  Google Scholar 

  30. Särkkä, S. and Nummenmaa, A., Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximation, IEEE Trans. Automat. Control, 2009, vol. 54, no. 3, pp. 596–600.

    Article  MathSciNet  Google Scholar 

  31. Tsyganova, J.V. and Kulikova, M.V., State Sensitivity Evaluation within UD Based Array Covariance Filter, IEEE Trans. Automat. Control, 2013, vol. 58, no. 11, pp. 2944–2950, DOI: 10.1109/TAC.2013.2259093.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. V. Kulikova.

Additional information

Original Russian Text © M.V. Kulikova, Yu.V. Tsyganova, 2014, published in Avtomatika i Telemekhanika, 2014, No. 8, pp. 59–81.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kulikova, M.V., Tsyganova, Y.V. A general approach to constructing parameter identification algorithms in the class of square root filters with orthogonal and J-orthogonal tranformations. Autom Remote Control 75, 1402–1419 (2014). https://doi.org/10.1134/S0005117914080050

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117914080050

Keywords

Navigation