Open AccessComparison between statistical and principal component analysis in reduction of near‐field FDTD data
Author(s) -
Ramos Glaucio L.,
Rodrigues Gustavo F.,
Camilo Franz M.,
Rego Cássio G.
Publication year - 2019
Publication title -
iet microwaves, antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.555
H-Index - 69
eISSN - 1751-8733
pISSN - 1751-8725
DOI - 10.1049/iet-map.2018.5611
Subject(s) - principal component analysis , finite difference time domain method , transformation (genetics) , field (mathematics) , algorithm , computer science , data compression , statistical model , reduction (mathematics) , mathematics , artificial intelligence , physics , optics , geometry , biochemistry , chemistry , pure mathematics , gene
We present a comparison of statistical and principal component analyses (PCA) for reducing the amount of near‐field data required as an input to a time‐domain spherical‐multipole near‐to‐far‐field transformation. Such transformations are necessary for finite‐difference time‐domain (FDTD) simulations that typically only model the near‐field. The authors demonstrate their approach for the case of far‐fields scattered by a dielectric sphere. For a threshold value of 10 6 , the PCA technique reduced the data required by 32%, using 12 components. A similar compression was achieved with statistical analysis, when the threshold is set at 10 − 7 σ . Their work shows that the proposed statistical compression is preferable because it has simpler implementation and low‐cost processing, when implemented together with the NFF transformation code.