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Approximate Maximum-likelihood Identification of Linear Systems from Quantized Measurements⁎
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2831-2909
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 724-729Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Elsevier B.V. , 2018. Vol. 51, no 15, p. 724-729
Keywords [en]
Least-squares approximation, Maximum-likelihood estimators, quantized signals, Impulse response, Least squares approximations, Linear systems, Approximation errors, Finite impulse response model, Identification of systems, Maximum a posteriori solutions, Maximum likelihood estimate, Maximum likelihood estimator, Quantized measurements, Variational approximation, Maximum likelihood estimation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-247498DOI: 10.1016/j.ifacol.2018.09.169ISI: 000446599200123Scopus ID: 2-s2.0-85054371213OAI: oai:DiVA.org:kth-247498DiVA, id: diva2:1305717
Note

QC20190418

Available from: 2019-04-18 Created: 2019-04-18 Last updated: 2019-05-22Bibliographically approved

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Risuleo, Riccardo SvenHjalmarsson, Håkan

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