In a recent paper, two least-squares (LS) based methods, which do not involve prefiltering of noisy measurements or parameter extraction, are established for unbiased identification of linear noisy input-output systems. This paper introduces more computationally efficient estimation schemes for the
On Linear Least-Squares Estimators for Continous-Time Stochastic Systems
β Scribed by John O'Reilly
- Publisher
- Elsevier Science
- Year
- 1979
- Tongue
- English
- Weight
- 657 KB
- Volume
- 307
- Category
- Article
- ISSN
- 0016-0032
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β¦ Synopsis
problem of least-squares state estimation of stochastic continuous-time linear systems is reconsidered. A concise derivation of the least-squares minimal-order estimator is presented using an innouations approach. An important result is the reinstatement of the problem in a least-squares estimation framework independent of deterministic obseruer theory. A second result is thus the clarification of previous approaches to the problem, particularly in relation to observer theory.
π SIMILAR VOLUMES
The least mean squared error linear one-stage predictor and filter are derived for discrete-time distributed parameter systems with uncertain observations. The measurements are taken at several fixed points of the spatial domain. We have used an orthogonal projection approach.