Gross errors are generally used to model intermittent sensor failures and occasional data packet losses or corruption, which arise in many engineering communities. This paper focuses on the problem of output-only recursive identification of time-varying systems subject to gross errors. Under the assumption that gross errors are unknown and can be of arbitrarily large magnitude, an adaptive recursive pseudo-linear regression time-dependent autoregressive moving average (TARMA) method is proposed by minimizing the sum of norm errors and achieving a sparse prediction error sequence. The proposed method is employed to identify a time-varying system and assessed against the existing recursive pseudo-linear regression TARMA method. The comparison demonstrates the superior achievable accuracy of the proposed method in extremely challenging circumstances.
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