Year: 2024
Type: article
Abstract: Interferences and computational cost pose significant challenges in large-scale interferometric sensing, impacting the accuracy and numerical efficiency of imaging algorithms. In this letter, we introduce an imaging algorithm using antenna switching based on expectation-maximization (EM) to address both challenges. By leveraging the low-rank noise model, our approach effectively captures interferences in interferometric data. Additionally, the proposed switching strategy between different sub-arrays reduces significantly the computational complexity during image restoration. Through extensive experiments on simulated datasets, we demonstrate the superiority of the low-rank noise model over the Gaussian noise model in the presence of interferences. Furthermore, we show that the proposed switching approach yields similar imaging performance with fewer antennas compared to the full array configuration, thereby reducing computational complexity, while outperforming non-switching configurations with the same number of antennas.
Source: IEEE Signal Processing Letters
Authors: Jianhua Wang, Mohammed Nabil El Korso, Lucien Bacharach, Pascal Larzabal
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404238201