Abstrakti
In the narrowband case, the best least squares approximation of a matrix by a unitary one is given by the Procrustes problem. In this paper, we expand this idea to matrices of analytic functions, and characterise a broadband equivalent to the narrowband case: the polynomial Procrustes problem. Its solution is based on an analytic singular value decomposition, and for the case of spectrally majorised, distinct singular values, we demonstrate the application of a suitable algorithm to three problems — time delay estimation, paraunitary matrix completion, and general paraunitary approximations — in simulations.
Alkuperäiskieli | Englanti |
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Otsikko | 31st European Signal Processing Conference (EUSIPCO 2023) |
Alaotsikko | Proceedings, 4 - 8 September 2023, Helsinki, Finland |
Kustantaja | IEEE |
Sivumäärä | 5 |
ISBN (elektroninen) | 978-9-4645-9360-0 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 2023 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
Tapahtuma | European Signal Processing Conference - Helsinki, Suomi Kesto: 4 syysk. 2023 → 8 syysk. 2023 Konferenssinumero: 31 https://eusipco2023.org/ |
Julkaisusarja
Nimi | European Signal Processing Conference |
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ISSN (elektroninen) | 2076-1465 |
Conference
Conference | European Signal Processing Conference |
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Lyhennettä | EUSIPCO |
Maa/Alue | Suomi |
Kaupunki | Helsinki |
Ajanjakso | 04/09/2023 → 08/09/2023 |
www-osoite |