Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique. (English) Zbl 1346.93384
Summary: This paper concerns the state and parameter estimation problem for an input nonlinear state-space system with colored noise. By using the data filtering and the over-parameterization technique, we transform the original nonlinear state-space system into two identification models with filtered states: one containing the system parameters and the other containing the noise model’s parameters. A combined state and parameter estimation algorithm is developed for identifying the state-space system. The key is that the estimation of system parameters uses the estimated states, and the estimation of states uses the preceding parameter estimates. A simulation example is provided to show that the proposed algorithm can work well.
MSC:
| 93E11 | Filtering in stochastic control theory |
Keywords:
filtering technique; recursive identification; state estimation; least squares; state-space modelReferences:
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