Compact order polynomial singular value decomposition of a matrix of analytic functions

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Abstract

This paper presents a novel method for calculating a compact order singular value decomposition (SVD) of polynomial matrices, building upon the recently proven existence of an analytic SVD for analytic, non-multiplexed polynomial matrices. The proposed method calculates a conventional SVD in sample points on the unit circle, and then applies phase smoothing algorithms to establish phase-coherence between adjacent frequency bins. This results in the extraction of compact order singular values and their corresponding singular vectors. The method is evaluated through experiments conducted on an ensemble of randomised polynomial matrices, demonstrating its superior performance in terms of higher decomposition accuracy and lower polynomial order compared to state-of-the-art techniques.
Original languageEnglish
Pages1-5
Number of pages5
Publication statusPublished - 13 Dec 2023
Event9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Los Suenos, Costa Rica
Duration: 10 Dec 202313 Dec 2023
https://camsap23.ig.umons.ac.be/

Workshop

Workshop9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP
Country/TerritoryCosta Rica
CityLos Suenos
Period10/12/2313/12/23
Internet address

Keywords

  • singular value decomposition
  • polynomial matrices
  • decomposition

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