A dynamic programming approach to trajectory estimation
- 1 July 1966
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automatic Control
- Vol. 11 (3) , 537-540
- https://doi.org/10.1109/tac.1966.1098348
Abstract
An iterative equation based on dynamic programming for finding the most likely trajectory of a dynamic system observed through a noisy measurement system is presented; the procedure can be applied to nonlinear systems with non-Gaussian noise. It differs from the recently developed Bayesian estimation procedure in that the most likely estimate of the entire trajectory up to the present time, rather than of the present state only, is generated. It is shown that the two procedures in general yield different estimates of the present state; however, in the case of linear systems with Gaussian noise, both procedures reduce to the Kalman-Bucy filter. Illustrative examples are presented, and the present procedure is compared with the Bayesian procedure and with other estimation techniques in terms of computational requirements and applicability.Keywords
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