Approximate maximum-likelihood identification of linear systems from quantized measurements

Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson

Research output: Contribution to journalConference articleAcademicpeer-review

3 Citations (Scopus)


We analyze likelihood-based identification of systems that are linear in the parameters from quantized output data; in particular, we propose a method to find approximate maximum-likelihood and maximum-a-posteriori solutions. The method consists of appropriate least-squares projections of the middle point of the active quantization intervals. We show that this approximation maximizes a variational approximation of the likelihood and we provide an upper bound for the approximation error. In a simulation study, we compare the proposed method with the true maximum-likelihood estimate of a finite impulse response model.

Original languageEnglish
Pages (from-to)724-729
Number of pages6
Issue number15
Publication statusPublished - 1 Jan 2018
Event18th IFAC Symposium on System Identification (SYSID 2018) - Stockholm, Sweden
Duration: 9 Jul 201811 Jul 2018


  • Least-squares approximation
  • Maximum-likelihood estimators
  • quantized signals


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