WE-A7.2P.2
Comparison of Deep-Learning Models for RF Array Calibration in the Presence of Phase Jitter
Richard Washington, Michael Saville, Richard Martin, U.S. Air Force (Air Force Institute of Technology), United States; Matthew Rustad, Nivia Colon-Diaz, U.S. Air Force (Air Force Research Laboratory), United States
Session:
Antenna Measurements: AI/ML and Digital Twin Applications Oral
Track:
AP-S: Track 7: Antenna Measurements
Location:
Room 354
Session Time:
Wed, 15 Jul, 13:20 - 15:00
Presentation Time:
Wed, 15 Jul, 13:40 - 14:00
Presentation
Discussion
Session WE-A7.2P
WE-A7.2P.1: Implicit Neural Representations for Learning a Broadband Antenna Pattern from Sparsely Sampled Data
Nataniel Holtzman, Tyrone Roach, Carl Boone, George Valley, Seth Berardinalli, Thomas Shaw, The Aerospace Corporation, United States
WE-A7.2P.2: Comparison of Deep-Learning Models for RF Array Calibration in the Presence of Phase Jitter
Richard Washington, Michael Saville, Richard Martin, U.S. Air Force (Air Force Institute of Technology), United States; Matthew Rustad, Nivia Colon-Diaz, U.S. Air Force (Air Force Research Laboratory), United States
WE-A7.2P.3: Artificial Intelligence for Seed Prediction for Phaseless Near-Field Measurements: Classification vs. Regression
Vignesh Manohar, Delart Technologies Corp, Canada; Yahya Rahmat-Samii, University of California Los Angeles, United States
WE-A7.2P.4: Performance Prediction of Transparent Meshed CP CubeSat Antennas Using ML Regression Models
Mohammad Atiqullah, Palash Kundu, Tabassum Islam, Ashraful Alam Shuvo, Saeed I. Latif, University of South Alabama, United States
WE-A7.2P.5: Broadband Modeling of RF Absorbers for Digital Twin Applications in Space Test Facilities
Leonardo Di Vincenzo, Leonardo Lizzi, University Of Trento, Italy; Daniela Munalli, Simone Lunardini, Giovanni Maria Mongini, Emiliano Scione, Thales Alenia Space Italia, Italy
Resources
No resources available.