Blind MIMO System Estimation Based on PARAFAC Decomposition of Higher Order Output Tensors
We present a novel framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. Samples of the system frequency response are obtained based on parallel factorization (PARAFAC) of three- or four-way tensors constructed b...
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| Published in | IEEE transactions on signal processing Vol. 54; no. 11; pp. 4156 - 4168 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.11.2006
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
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| Abstract | We present a novel framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. Samples of the system frequency response are obtained based on parallel factorization (PARAFAC) of three- or four-way tensors constructed based on, respectively, third- or fourth-order cross spectra of the system outputs. The main difficulties in frequency-domain methods are frequency-dependent permutation and filtering ambiguities. We show that the information available in the higher order spectra allows for the ambiguities to be resolved up to a constant scaling and permutation ambiguities and a linear phase ambiguity. Important features of the proposed approach are that it does not require channel length information, needs no phase unwrapping, and unlike the majority of existing methods, needs no prewhitening of the system outputs |
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| AbstractList | We present a novel framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. Samples of the system frequency response are obtained based on parallel factorization (PARAFAC) of three- or four-way tensors constructed based on, respectively, third- or fourth-order cross spectra of the system outputs. The main difficulties in frequency-domain methods are frequency-dependent permutation and filtering ambiguities. We show that the information available in the higher order spectra allows for the ambiguities to be resolved up to a constant scaling and permutation ambiguities and a linear phase ambiguity. Important features of the proposed approach are that it does not require channel length information, needs no phase unwrapping, and unlike the majority of existing methods, needs no prewhitening of the system outputs |
| Author | Petropulu, A.P. Yuanning Yu Acar, T. |
| Author_xml | – sequence: 1 givenname: T. surname: Acar fullname: Acar, T. organization: Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA – sequence: 2 surname: Yuanning Yu fullname: Yuanning Yu – sequence: 3 givenname: A.P. surname: Petropulu fullname: Petropulu, A.P. |
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| Cites_doi | 10.1007/BF02310791 10.1109/TSP.2004.838938 10.1109/78.942635 10.1016/0165-1684(94)90029-9 10.1016/S0267-7261(98)00005-0 10.1109/29.21704 10.1109/TSP.2003.811234 10.1109/78.552228 10.1109/78.554307 10.1109/TSP.2003.811232 10.1002/(SICI)1099-128X(199805/06)12:3<155::AID-CEM502>3.0.CO;2-5 10.1109/78.558487 10.1109/58.646925 10.1109/TASSP.1981.1163714 10.1016/0024-3795(77)90069-6 10.1109/5.720247 10.1109/78.348133 10.1016/S0165-1684(97)00065-0 10.1109/TIP.2003.811512 10.1109/78.934137 10.1049/ip-f-2.1993.0054 10.1109/78.815476 10.1109/IEMBS.1993.978564 10.1155/S1110865702000823 10.1109/18.556108 10.1109/78.552212 10.1109/78.824675 10.1109/78.558482 10.1002/cem.839 10.1109/TSP.2004.832022 10.1109/78.823978 10.1007/BF02288739 10.1109/ICASSP.1995.478484 10.1002/1099-128X(200005/06)14:3<229::AID-CEM587>3.0.CO;2-N 10.1109/78.950782 10.1049/ip-rsn:19982224 10.1109/TSP.2003.821108 10.1109/97.508167 10.1109/97.586043 10.1109/78.599996 |
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| Keywords | MIMO system Phase unwrapping Filtering Image processing Third order Linear phase Frequency domain method Blind identification Image restoration multiple-input multiple-output (MIMO) Factorization Frequency response System identification convolutive MIMO parallel factorization (PARAFAC) Higher order statistics Blind system identification |
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| SubjectTerms | Ambiguity Applied sciences Blind system identification Channels convolutive MIMO Detection, estimation, filtering, equalization, prediction Exact sciences and technology Factorization Filtering Frequency domain analysis Frequency response Higher order statistics Image processing Independent component analysis Information, signal and communications theory Mathematical analysis MIMO Multiaccess communication multiple-input multiple-output (MIMO) parallel factorization (PARAFAC) Permutations Signal and communications theory Signal processing Signal, noise Spectra Speech processing System identification Telecommunications and information theory Tensile stress Tensors |
| Title | Blind MIMO System Estimation Based on PARAFAC Decomposition of Higher Order Output Tensors |
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