Partitioning: A unifying framework for adaptive systems, I: Estimation
- 1 January 1976
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Proceedings of the IEEE
- Vol. 64 (8) , 1126-1143
- https://doi.org/10.1109/proc.1976.10284
Abstract
In this paper, partitioning and the associated generalized partitioned estimation algorithms are shown to constitute a unifying and powerful framework for optimal adaptive estimation in linear as well as nonlinear problems. Using the partitioning framework, the adaptive estimation problem is treated from a global viewpoint that readily yields and unifies seemingly unrelated results and, most importantly, yields fundamentally new families of nonlinear and linear estimation algorithms in a decoupled parallel-realization form. The generalized partitioned estimation algorithms are shown to have several important properties from both a theoretical and a realization or computational standpoint.Keywords
This publication has 53 references indexed in Scilit:
- A unifying framework for linear estimation: Generalized partitioned algorithmsInformation Sciences, 1976
- Partitioned Ricatti solutions and integration-free doubling algorithmsIEEE Transactions on Automatic Control, 1976
- On the development of practical nonlinear filtersInformation Sciences, 1974
- Partitioned estimation algorithms, II: Linear estimationInformation Sciences, 1974
- Gaussian Sum Approximations in Nonlinear Filtering and ControlInformation Sciences, 1974
- Partitioned estimation algorithms, I: Nonlinear estimationInformation Sciences, 1974
- Estimation: A brief surveyInformation Sciences, 1974
- Nonlinear Bayesian estimation using Gaussian sum approximationsIEEE Transactions on Automatic Control, 1972
- Dynamical equations for optimal nonlinear filteringJournal of Differential Equations, 1967
- Optimal adaptive estimation of sampled stochastic processesIEEE Transactions on Automatic Control, 1965