Compressive Phaseless Imaging Based on Synthetic Aperture Radar Techniques

This contribution presents a method to simplify the instrumentation required in an electromagnetic imaging system by processing scalar data. Starting from a reduced number of amplitude-only scattered field measurements, and combining phaseless algorithms with the compressive sensing framework, a ref...

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Bibliographic Details
Authors: Hoyo Vijande, Alejandro del|||0009-0003-2399-2428, Álvarez López, Yuri|||0000-0003-3625-4515, Laviada Martínez, Jaime, Las Heras Andrés, Fernando Luis
Format: article
Publication Date:2025
Country:España
Institution:Universidad de Oviedo (UNIOVI)
Repository:RUO. Repositorio Institucional de la Universidad de Oviedo
Language:English
OAI Identifier:oai:digibuo.uniovi.es:10651/79199
Online Access:https://ieeexplore.ieee.org/abstract/document/10969101
https://hdl.handle.net/10651/79199
https://dx.doi.org/10.1109/TIM.2025.3561369
Access Level:Open access
Keyword:Compressive Sensing (CS)
Electromagnetic Imaging
Phaseless Imaging
Radar Signal Processing
Synthetic Aperture Radar (SAR)
Description
Summary:This contribution presents a method to simplify the instrumentation required in an electromagnetic imaging system by processing scalar data. Starting from a reduced number of amplitude-only scattered field measurements, and combining phaseless algorithms with the compressive sensing framework, a reflectivity image of the region of interest can be generated. The complexity of the system shifts from the hardware components involved in the measurement stage to the processing algorithms, allowing for the use of more cost-effective equipment. The proposed method has been tested both in simulation and measurement. In order to reach the convergence of the algorithms, the method exploits the information of the scattered field in four different planes, located at different distances from the plane containing the targets. Besides, solving the phaseless problem in terms of spatial frequency domain basis functions allows for a significant reduction in the number of unknowns, which results in an improvement in the convergence of the phaseless iterative methods. Satisfactory results have been obtained using only 50% of the measurements required to uniformly sample the field according to Nyquist-Shannon criterion.