Titre : | Compressed Sensing for Microwave In-Depth Imaging |
Auteurs : | Mathias BECQUAERT, Auteur ; Marijke VANDEWAL, Directeur de thèse ; Johan Stiens, Directeur de thèse |
Type de document : | Thesis |
Editeur : | Brussels [Belgique] : Royal Military Academy, 2019 |
Langues: | Anglais |
Catégories : |
2.20 Sciences de la physique > Magnétisme > Onde électromagnétique |
Résumé : |
Low hardware costs, short measurement times, fast data handling, and flexibility in taking measurements under harsh conditions, are some of the key requirements of today’s sensors.
These sensor characteristics are influenced or determined by the number of measurements. The classic sampling theory fixes a lower bound on the number of samples needed, known as the Shannon-Nyquist sampling rate. Over the past decade, a new measurement method?ology has gained a lot of attention: Compressed Sensing (CS). The advent of CS is motivated by the observation that in many applications the measurements, performed at high sampling rates and thus generating high data volumes, are followed by a compression step. Compressed Sensing incorporates the compression step into the measurement phase. Under certain con?ditions on the signal sparsity and on the measurement methodology, CS allows to reconstruct the sensed signal from a number of samples far below the Shannon-Nyquist bound. In this work, Compressed Sensing is applied on Stepped-Frequency Continuous Wave (SFCW) and Synthetic Aperture Radar (SAR) measurements. In our work, the applicability of Compressed Sensing on SFCW and SAR data is evaluated for two applications: (1) The Non-Destructive Testing (NDT) of 3D-printed objects and (2) Through-the-Wall Radar Imaging (TWRI). 3D-printing or Additive Manufacturing (AM) is... |
En ligne : | http://www.sic.rma.ac.be/phd/pdf/becquaert19.pdf |
Documents numériques (1)
315483R URL |