A general hankel-norm approximation scheme for linear recursive filtering

Andrea Gombani, Michele Pavon

Research output: Contribution to journalArticlepeer-review


Let {x(t)} and {y(t)} be stochastic processes which are weakly stationary and stationarily correlated. We consider the problem of finding an approximate recursive low-dimensional filter of x(t), based on the observation of the past of y(t), using Hankel-norm techniques. Several estimation problems have been investigated in the past using these techniques. We present here a general framework which includes many of these approaches as special cases. We also discuss some new applications. The approximate filter so constructed allows for an a priori bound on the estimation error.

Original languageEnglish (US)
Pages (from-to)103-112
Number of pages10
Issue number1
StatePublished - Jan 1990


  • Hankel-norm approximation (not in the standard list)
  • model reduction
  • random process
  • Recursive estimation
  • smoothing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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