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A new log-likelihood gradient formula for continuous time stochastic systems with uncertain matrix

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1 Author(s)
R. P. Leland ; Dept. of Electr. Eng., Alabama Univ., Tuscaloosa, AL, USA

We use a finitely additive white noise approach to derive an explicit expression for the gradient of the log-likelihood ratio for system parameter estimation in the case of a continuous time linear dynamic stochastic system and noisy observations. Our gradient formula, includes the smoother estimates of the state, and derivatives of the system matrices, but no derivatives of the estimates or error covariances. A scheme to calculate the log-likelihood gradient without solving any Ricatti equations is described

Published in:

Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on  (Volume:3 )

Date of Conference:

14-16 Dec 1994