Abstract
The numerical instabilities of the bias compensated least-squares (BCLS) algorithm are discussed in the case where input and output measurements are corrupted by white noise. For improving the stability of BCLS algorithm, an estimator constructed by filtered data vectors is developed. The identifiability in the special case where input signal is white is also discussed. Some simulation results are presented to demonstrate the numerical robustness of the proposed BCLS algorithm
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