Machine Learning Sensor Fusion for Water Vapor Radiometry based Tropospheric Delay Calibration
Abstract
The next-generation Very Large Array (ngVLA) intends to use 22 GHz water vapor radiometry (WVR) to correct for tropospheric phase fluctuations in its high-frequency bands. However, current delay-retrieval methods do not reach the required accuracy and reliability. As a remedy, we propose a machine-learning based retrieval concept and a preliminary architecture, framed as heterogeneous, multi-rate sensor fusion. Atmospheric reanalysis profiles and surface weather set the context in which per-antenna WVR spectra are used to estimate excess path by a Kalman filter whose parameters are learned from high-cadence calibrator phases. The method is being developed, with planned first validation on the 183 GHz WVR system of the Atacama Large Millimeter/submillimeter Array (ALMA).
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BibTeX
@misc{sridharan2026machine,
title = {{Machine Learning Sensor Fusion for Water Vapor Radiometry based Tropospheric Delay Calibration}},
author = {T. K. Sridharan and Yoshiharu Asaki and Brian E. Svoboda},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41052},
eprint = {2610.41052},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41052},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Machine Learning Sensor Fusion for Water Vapor Radiometry based Tropospheric Delay Calibration
AU - T. K. Sridharan
AU - Yoshiharu Asaki
AU - Brian E. Svoboda
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.41052
UR - https://arxiv.org/abs/2610.41052
AB - The next-generation Very Large Array (ngVLA) intends to use 22 GHz water vapor radiometry (WVR) to correct for tropospheric phase fluctuations in its high-frequency bands. However, current delay-retrieval methods do not reach the required accuracy and reliability. As a remedy, we propose a machine-learning based retrieval concept and a preliminary architecture, framed as heterogeneous, multi-rate sensor fusion. Atmospheric reanalysis profiles and surface weather set the context in which per-antenna WVR spectra are used to estimate excess path by a Kalman filter whose parameters are learned from high-cadence calibrator phases. The method is being developed, with planned first validation on the 183 GHz WVR system of the Atacama Large Millimeter/submillimeter Array (ALMA).
ER -