Physics-informed deep learning-based adaptive beamforming for phased array weather radar.

Area of Science:

• Radar Systems Engineering
• Artificial Intelligence in Meteorology
• Computational Electromagnetics

Background:

• Phased array weather radar systems require efficient beamforming for accurate target detection.
• Existing deep learning methods may lack physical grounding, impacting performance in complex scenarios.
• Large antenna element configurations present computational challenges for traditional beamforming.

Purpose of the Study:

• To develop a novel physics-informed deep learning beamforming framework for phased array weather radar.
• To enhance computational efficiency and reliability in weather target detection.
• To validate the framework's performance against established machine learning models and real-world data.

Main Methods:

• A physics-informed deep neural network (PIDNN) beamformer was developed, incorporating physical constraints via a custom loss function.
• The PIDNN was trained to minimize the discrepancy between predicted and reference array outputs.
• Performance was benchmarked against radial basis function neural networks (RBFNNs) and convolutional neural networks (CNNs).

Main Results:

• The PIDNN beamformer demonstrated superior accuracy and computational efficiency compared to RBFNN and CNN.
• Real-world data from the PAR@OU system validated the PIDNN's practical applicability.
• The PIDNN achieved rapid, accurate reflectivity estimation and superior clutter suppression.

Conclusions:

• Physics-informed deep learning offers a powerful approach for advanced beamforming in weather radar.
• The PIDNN framework provides a computationally efficient and accurate solution for weather target detection and clutter mitigation.
• This methodology holds significant potential for improving weather radar system performance.