This study develops an adaptivefiltering-based recursive identification algorithm for joint estimation of states and parameters of bilinear state-space systems with an autoregressive moving average noise. In order to...
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This study develops an adaptivefiltering-based recursive identification algorithm for joint estimation of states and parameters of bilinear state-space systems with an autoregressive moving average noise. In order to handle the correlated noise and unmeasurable states in parameter estimation, an adaptive filter is established to whiten the coloured noise and a bilinear state observer is constructed to update the unavailable states recursively. Then a hierarchical generalised extended least squares (hgels) algorithm and an adaptive filtering-based hgels algorithm are developed for simultaneously estimating the unknown states and parameters. The convergence analysis indicates that the parameter estimates can converge to their true values. A numerical example illustrates the convergence results.
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