Polynomial Procrustes Solution for Randomly Perturbed Near-Paraunitary Systems
Weiss S, Schlecht S, Moonen M (2025)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2025
Publisher: IEEE Computer Society
Pages Range: 231-235
Conference Proceedings Title: 2025 IEEE Statistical Signal Processing Workshop (SSP)
Event location: Edinburgh
ISBN: 979-8-3315-1801-1
DOI: 10.1109/SSP64130.2025.11073288
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.
Authors with CRIS profile
Involved external institutions
University of Strathclyde
United Kingdom (GB)
Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven
Belgium (BE)
United Kingdom (GB)
Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven
Belgium (BE)
How to cite
APA:
Weiss, S., Schlecht, S., & Moonen, M. (2025). Polynomial Procrustes Solution for Randomly Perturbed Near-Paraunitary Systems. In 2025 IEEE Statistical Signal Processing Workshop (SSP) (pp. 231-235). Edinburgh, GB: IEEE Computer Society.
MLA:
Weiss, Stephan, Sebastian Schlecht, and Marc Moonen. "Polynomial Procrustes Solution for Randomly Perturbed Near-Paraunitary Systems." Proceedings of the 2025 IEEE Statistical Signal Processing Workshop, SSP 2025, Edinburgh IEEE Computer Society, 2025. 231-235.
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