The main objective of this project is to develop an anomaly detection framework specifically tailored for Nitrogen-Vacancy (NV) quantum sensor systems in Position, Naviga-tion, and Timing (PNT) applications. This framework will fuse data from both quantum and classical sensors, with particular attention to detecting inherent quantum sensor anomalies. The system will employ AI-driven methodologies to process and analyse multi-sensor data streams, aiming to outperform conventional machine learning approaches in PNT applications.

The technical implementation consists of three main phases:
1. Simulation environment development
2. Hardware integration and sensor fusion
3. Adaptive sensor fusion algorithm