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- Title
Deep Learning Method for Evaporation Duct Inversion Based on GPS Signal.
- Authors
Cui, Ming-Yu; Zhang, Yu
- Abstract
Accurate evaporation duct prediction is one of the critical technologies for realizing the over-the-horizon impact of marine communication, ship radar, and other systems. Using GPS signals to invert evaporation ducts provides more benefits in terms of method realization and ease. In order to invert the evaporation duct from GPS-received power data, a deep learning technique based on Bayesian optimization is proposed to increase the prediction accuracy of evaporation ducts. The evaporation duct propagation mechanism of the GPS signal is explored. The GPS-received power is estimated using the two-parameter evaporation duct model, and a better neural network structure is built using Bayesian optimization. The study results show that the Bayesian optimization model has a smaller root mean square error (RMSE) than the human empirical model, which allows for rapid and accurate inversion of duct parameters even in noisy interference.
- Subjects
DEEP learning; STANDARD deviations; MARINE communication; GLOBAL Positioning System
- Publication
Atmosphere, 2022, Vol 13, Issue 12, p2091
- ISSN
2073-4433
- Publication type
Article
- DOI
10.3390/atmos13122091