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
Estimation technique using covariance information in linear discrete-time systems
β Scribed by Seiichi Nakamori
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 758 KB
- Volume
- 43
- Category
- Article
- ISSN
- 0165-1684
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A discrete one-stage predictor algorithm using covariance information in linear systems is derived. The algorithm is obtained for white Gaussian observation noise. The signal is a nonstationary or stationary stochastic process. The autocovariance function of the signal is expressed using a semidegen
The estimation problem of a signal is considered for the white Gaussian observation noise in linear continuous systems. At first, the recursive fixed-point smoother and filter are designed using the covariance information. The observation equation is given by y(t) = z(t) + v(t), z(t) = H(t)z(t), whe