Title :
Optimal adaptive estimation: Structure and parameter adaptation
Author_Institution :
The University of Texas at Austin, Austin, Texas
Abstract :
Optimal structure and parameter adaptive estimators have been obtained for continuous as well as discrete data gaussian process models with linear dynamics. Specifically, the essentially nonlinear adaptive estimators are shown to be decomposable (partition theorem) into two parts, a linear non-adaptive part consisting of a bank of Kalman-Bucy filters, and a nonlinear part that incorporates the learning or adaptive nature of the estimator. The conditional-error-covariance matrix of the estimator is also obtained in a form suitable for on-line performance evaluation. The adaptive estimators are applied to the problem of state-estimation with nongaussian initial state and also to estimation under measurement uncertainty (joint detection-estimation). Examples are given of the application of the proposed adaptive estimators to structure and parameter adaptation indicating their applicability to practical engineering problems.
Keywords :
Adaptive estimation; Adaptive filters; Covariance matrix; Filter bank; Gaussian processes; Matrix decomposition; Nonlinear filters; Parameter estimation; Random processes; State estimation;
Conference_Titel :
Adaptive Processes (9th) Decision and Control, 1970. 1970 IEEE Symposium on
Conference_Location :
Austin, TX, USA
DOI :
10.1109/SAP.1970.269994