RTX Classification of RF Signals Captured in Flight using GPU-Accelerated Python Scripts

Project Description:

This capstone project, a continuation of the RTX Fall 2024 - Spring 2025 project, is designed for undergraduate students specializing in data science and related fields. The primary objective is to develop GPU-accelerated Python scripts capable of classifying RF signals captured during flight. The project will incorporate advanced data science techniques, signal processing, and machine learning algorithms to achieve its goals, leveraging the CADS (Cognitive Algorithm Deployment System) platform and collaborating with members of the CADS team.

Project Objectives:

1. Develop GPU-Accelerated Python Scripts:

• Utilize GPU programming to enhance the performance of signal classification algorithms.
• Implement and optimize Python scripts for real-time processing of RF signals.

2. Classify RF Signals:

• Analyze RF signals training set comprised of MATLAB Simulation data
• Develop classification models to accurately identify different types of RF signals.

3. Test and Validate Models:

• Conduct rigorous testing to ensure the reliability and accuracy of the classification models.
• Validate the performance of the models using a manually collected data set of RF signals.

4. Flight Testing:

• If the project reaches a mature stage by mid-second semester, conduct a test flight in Tucson, AZ, to collect real-world RF signal data.
• Deploy and test the developed application during the flight to evaluate its performance in a live environment.