Work

Signal Estimation from Correlation Vectors with Structured Low-Rank Approximation

Year: 2025

Type: conference-paper

Abstract: The paper focuses on the reconstruction of a multivariate signal from its noisy correlation vectors, a problem appearing in phase retrieval and blind channel identification. In the noiseless case, the signals can be retrieved from greatest common divisors of the polynomials associated to observed vectors. For the noisy case, by exploiting the properties of Sylvester matrices, we propose a new reconstruction approach that exploits all the available data and preserves matrix structure in the associated low-rank approximation problem. By doing so, we achieve an improvement of the reconstruction performance with respect to existing methods.

Source: European Signal Processing Conference (EUSIPCO)

DOI: 10.23919/eusipco63237.2025.11226430

Authors: Antonio Fazzi, Julien Flamant, Konstantin Usevich

Institutions: Centre National de la Recherche Scientifique, CRAN, Université de Lorraine

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Bivariate signal processing: a geometric approach to decipher polarization