Bias Compensated Recursive Least-squares Identification for the Systems under Poor Observation Condition

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For the autoregressive signal under poor observation condition, a bias compensated recursive least-square identification algorithm is presented based on the incremental observation equation. Moreover, it is easy to know that the obtained estimates of model parameters and noise variance converge to the corresponding true values with probability one, i.e. they are strongly consistent. A simulation example shows its effectiveness.