Efficient Neural Network-Based Reconstruction of Three-Dimensional Antenna Radiation Patterns From Two-Dimensional Cuts
This paper presents a novel solution to the classical problem of interpolating three-dimensional antenna radiation patterns from two-dimensional, orthogonal pattern slices. We introduce a neural network model that performs this interpolation with high accuracy across a wide range of patterns, including cases where conventional interpolation methods struggle. This model is beneficial for three-dimensional modeling methods, such as ray-tracing, where a full antenna radiation pattern is needed, whereas only principal plane patterns are available from measurements or data sheets. The proposed neural network combines computational efficiency with superior accuracy compared to available alternative methods.
Authors
- Yuanzhi Liu (ORCID: https://orcid.org/0000-0002-4740-7874)
- S. Jan
- Costas D. Sarris (ORCID: https://orcid.org/0000-0003-4857-8330)
Institutions
- University of Toronto (CA)
Publication Details
- Journal
- IEEE Open Journal of Antennas and Propagation
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1109/ojap.2025.3544968
- Primary Topic
- Antenna Design and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Sciences and Engineering Research Council of Canada