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Polynomial Procrustes solution for randomly perturbed near-paraunitary systems

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Abstract

We want to recover paraunitary matrices under small random perturbations. The polynomial Procrustes method, based on the analytic singular value decomposition of the perturbed system, in principle solves this. For small random perturbations, where the analytic singular values are close to unity, we propose a simplified polynomial Procrustes method that exploits this property, but show that the support of the solution is generally increased compared to the perturbed matrix. We therefore embed the simplified Procrustes method into an iterative truncation scheme, which can reduce the support while ensuring that a paraunitary approximation remains within a perimeter that is equivalent to the level of perturbation.
Original languageEnglish
Title of host publication2025 IEEE Statistical Signal Processing Workshop (SSP)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages231-235
Number of pages5
ISBN (Electronic)9798331518004
ISBN (Print)9798331518011
DOIs
Publication statusPublished - 16 Jul 2025
Event23rd IEEE Statistical Signal Processing Workshop - Edinburgh, United Kingdom
Duration: 8 Jun 202511 Jun 2025
https://2025.ieeessp.org/

Publication series

NameIEEE/SP Workshop on Statistical Signal Processing (SSP)
ISSN (Print)2373-0803
ISSN (Electronic)2693-3551

Conference

Conference23rd IEEE Statistical Signal Processing Workshop
Abbreviated titleSSP 2025
Country/TerritoryUnited Kingdom
CityEdinburgh
Period8/06/2511/06/25
Internet address

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • polynomial Procrustes method
  • near-paraunitary systems
  • paraunitary matrices
  • perturbed systems
  • random perturbations

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