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- W1992877275 abstract "This paper derives algorithms for identifying autoregressive models, with external input, of multi-input multi-output systems from data using a fast transversal filter or a least-squares lattice filter. The autoregressive models including external inputs are used to identify state-space models and the corresponding observer/Kalman filter gains of the system. The derivation is an extension of scalar autoregressive model approaches, modified to cope with multivariables, external inputs and an extra direct-influence term. Comparisons between the fast transversal filter, the least-squares lattice filter and the classical least-squares method are made in terms of complexity, computational cost and practical applications issues. A numerical example is included to illustrate the approach." @default.
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- W1992877275 date "1992-08-10" @default.
- W1992877275 modified "2023-10-16" @default.
- W1992877275 title "Several recursive techniques for observer/Kalman filter system identification from data" @default.
- W1992877275 doi "https://doi.org/10.2514/6.1992-4386" @default.
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