Reduced-Order Estimation Technique Using Covariance Data of Observed Value in Linear Discrete-Time Systems
✍ Scribed by Seiichi Nakamori
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 182 KB
- Volume
- 7
- Category
- Article
- ISSN
- 1051-2004
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✦ Synopsis
This paper proposes reduced-order estimation technique by the recursive least-squares filter and fixed-point smoother in linear discrete-time systems, given output measurement data. The estimators require the information of the system matrix, the observation vector of the signal generating model and the variances of the state and white Gaussian observation noise processes. This paper shows that these necessary quantities are calculated in terms of the output measurement data for the signal process modeled by the AR (autoregressive) process of order n. As a consequence, we can estimate the signal via the estimators in terms of output measurement data. Furthermore, to shorten the computer implementation time, a reduced-order estimation technique is proposed by decreasing the model order in comparison with the optimum order of the AR model. A numerical simulation example is examined to ascertain that the estimation accuracy based on the reduced-order AR model is almost equivalent to that based on the AR model of optimum order.