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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Detalles Bibliográficos
Autores: Hoyo Vijande, Alejandro del|||0009-0003-2399-2428, Álvarez López, Yuri|||0000-0003-3625-4515, Laviada Martínez, Jaime, Las Heras Andrés, Fernando Luis
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Oviedo (UNIOVI)
Repositorio:RUO. Repositorio Institucional de la Universidad de Oviedo
Idioma:inglés
OAI Identifier:oai:digibuo.uniovi.es:10651/79199
Acceso en línea:https://ieeexplore.ieee.org/abstract/document/10969101
https://hdl.handle.net/10651/79199
https://dx.doi.org/10.1109/TIM.2025.3561369
Access Level:acceso abierto
Palabra clave:Compressive Sensing (CS)
Electromagnetic Imaging
Phaseless Imaging
Radar Signal Processing
Synthetic Aperture Radar (SAR)
Descripción
Sumario: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.