We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
On the Optimal Field Sensing in Near-Field Characterization.
- Authors
Capozzoli, Amedeo; Curcio, Claudio; Liseno, Angelo
- Abstract
We deal with the problem of characterizing a source or scatterer from electromagnetic radiated or scattered field measurements. The problem refers to the amplitude and phase measurements which has applications also to interferometric approaches at optical frequencies. From low frequencies (microwaves) to high frequencies or optics, application examples are near-field/far-field transformations, object restoration from measurements within a pupil, near-field THz imaging, optical coherence tomography and ptychography. When analyzing the transmitting-sensing system, we can define "optimal virtual" sensors by using the Singular Value Decomposition (SVD) approach which has been, since long time, recognized as the "optimal" tool to manage linear algebraic problems. The problem however emerges of discretizing the relevant singular functions, thus defining the field sampling. To this end, we have recently developed an approach based on the Singular Value Optimization (SVO) technique. To make the "virtual" sensors physically realizable, in this paper, two approaches are considered: casting the "virtual" field sensors into arrays reaching the same performance of the "virtual" ones; operating a segmentation of the receiver. Concerning the array case, two ways are followed: synthesize the array by a generalized Gaussian quadrature discretizing the linear reception functionals and use elementary sensors according to SVO. We show that SVO is "optimal" in the sense that it leads to the use of elementary, non-uniformly located field sensors having the same performance of the "virtual" sensors and that generalized Gaussian quadrature has essentially the same performance.
- Subjects
SINGULAR value decomposition; OPTICAL coherence tomography; SENSOR arrays; ALGEBRAIC field theory; MARKOV random fields
- Publication
Sensors (14248220), 2021, Vol 21, Issue 13, p4460
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21134460