Abstract
New numerical algorithms for differentiating matrix orthogonal transformations are constructed. They do not require that the derivatives of the orthogonal transformation matrix be available. An example is given how these algorithms can be applied to the numerically stable calculation of a solution to the discrete-time matrix Riccati sensitivity equation.
Similar content being viewed by others
References
V. V. Voevodin, Computational Foundations of Linear Algebra (Nauka, Moscow, 1977) [in Russian].
A. A. Samarskii and A. V. Gulin, Numerical Methods (Nauka, Moscow, 1989) [in Russian].
J. R. Rice, Matrix Computations and Mathematical Software (McGraw-Hill, New York, 1981; Mir, Moscow, 1984).
I. V. Semushin, Numerical Methods in Algebra (Ul’yanov. Gos. Tekh. Univ., Ul’yanovsk, 2006) [in Russian].
G. H. Golub and C. F. van Loan, Matrix Computations (Johns Hopkins Univ. Press, Baltimore, Md., 1996; Mir, Moscow, 1999).
T. Kailath, A. H. Sayed, and B. Hassibi, Linear Estimation (Prentice Hall, Englewood Cliffs, N.J., 2000).
G. J. Bierman, Factorization Methods for Discrete Sequential Estimation (Academic, New York, 1977).
M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 2nd ed. (Wiley, London, 2001).
M. Veriaegen and P. Van Dooren, “Numerical aspects of different Kalman filter implementations,” IEEE Trans. Autom. Control 31 (10), 907–917 (1986).
P. C. Kaminski, A. E. Bryson, and S. F. Schmidt, “Discrete square root filtering: A survey of current techniques,” IEEE Trans. Autom. Control 16 (6), 727–735 (1971).
I. V. Semushin, Yu. V. Tsyganova, M. V. Kulikova, O. A. Fat’yanova, and A. E. Kondrat’ev Adaptive Systems for Filtering, Control, and Fault Detection (Ul’yanov. Gos. Tekh. Univ., Ul’yanovsk, 2011) [in Russian].
N. K. Gupta and R. K. Mehra, “Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations,” IEEE Trans. Autom. Control 19 (6), 774–783 (1974).
K. J. Aström, “Maximum likelihood and prediction error methods,” Automatica 16, 551–574 (1980).
G. J. Bierman, M. R. Belzer, J. S. Vapdergraft, and D. W. Porter, “Maximum likelihood estimation using square root information filters,” IEEE Trans. Autom. Control 35 (12), 1293–1299 (1990).
M. V. Kulikova, “Likelihood gradient evaluation using square-root covariance filters,” IEEE Trans. Autom. Control 54 (3), 646–651 (2009).
M. V. Kulikova, “Maximum likelihood estimation via the extended covariance and combined square-root filters,” Math. Comput. Simul. 79, 1641–1657 (2009).
M. V. Kulikova, “Maximum likelihood estimation of linear stochastic systems in the class of sequential squareroot orthogonal filtering methods,” Autom. Remote Control 72 (4), 766–786 (2011).
I. V. Semushin and Yu. V. Tsyganova, “Adaptive square-root covariance algorithm for filtering in navigation systems,” Proceedings of International Conference on Promising Information Technologies in Aviation and Space (PIT-2010) (Samara, 2010), pp. 118–122.
Yu. V. Tsyganova, “Computing the gradient of the auxiliary quality functional in the parametric identification problem for stochastic systems,” Autom. Remote Control 72 (9), 1925–1940 (2011).
Yu. V. Tsyganova and M. V. Kulikova, “On efficient parametric identification methods for linear discrete stochastic systems,” Autom. Remote Control 73 (6), 962–975 (2012).
L. Died and T. Eirola, “On smooth decompositions of matrices,” SIAM. J. Matrix Anal. Appl. 20 (3), 800–819 (1999).
M. A. Ogarkov, Methods for Statistical Parameter Estimation in Stochastic Processes (Energoatomizdat, Moscow, 1990) [in Russian].
P. Park and T. Kailath, “New square-root algorithms for Kalman filtering,” IEEE Trans. Autom. Control 40 (5), 895–899 (1995).
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © M.V. Kulikova, Yu.V. Tsyganova, 2015, published in Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 2015, Vol. 55, No. 9, pp. 1460–1473.
Rights and permissions
About this article
Cite this article
Kulikova, M.V., Tsyganova, Y.V. Differentiating matrix orthogonal transformations. Comput. Math. and Math. Phys. 55, 1419–1431 (2015). https://doi.org/10.1134/S0965542515090109
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0965542515090109