Physics-Informed Deep Learning for Drone RCS Prediction and Classification Overview This repository contains the LaTeX source code for the research paper titled "Physics-Informed Deep Learning for Drone RCS Prediction and Classification: A Multi-Frequency Multi-Angular Analysis". The paper introduces a Physics-Informed Swin Transformer (PiSwinTransformer) architecture for predicting and classifying the Radar Cross-Section (RCS) of drones using multi-frequency and multi-angular electromagnetic data.
Key Features
Physics-Informed Deep Learning: Integrates electromagnetic theory with deep learning for accurate RCS prediction and drone classification. Multi-Frequency Analysis: Processes data across 1-6 GHz to capture frequency-dependent scattering. Multi-Angular Analysis: Uses spherical harmonic embeddings to model angular dependencies.