Detection and Analysis of Anomalies in Tactical Sensor Systems through Structured Hypothesis Testing

The project explores the domain of tactical sensor systems, focusing on SAAB Gripen's sensor technologies such as radar, RWR, and IRST. The study employs structured hypothesis testing and model-based diagnostics to examine the effectiveness of identifying and isolating deviations within these systems. The central question addressed is whether structured hypothesis testing reliably detects and isolates anomalies in a tactical sensor system.

The research employs a framework involving sensor modeling of radar, RWR (Radar Warning Receiver), and IRST (InfraRed Search and Track), alongside a sensor fusion model, applied on a linear target tracking model as well as a real target flight track obtained from SAAB Test Flight and Verification. Test quantities are derived from the modeled data, and synthetic faults are intentionally introduced into the system. These test quantities are then compared to predefined thresholds, thereby facilitating structured hypothesis testing.

The robustness and reliability of the diagnostics model are established through a series of simulations. Multiple scenarios with varied fault introductions across different sensor measurements are examined. 

Key results includes the successful creation of a tactical sensor model and sensor fusion environment, showcasing the ability to introduce and detect faults. The thesis provides arguments supporting the advantages of model-based diagnosis through structured hypothesis testing for assessing sensor fusion data. The results of this research is applicable beyond this specific context, facilitating improved sensor data analysis across diverse tracking scenarios, including applications beyond SAAB Gripen.

As sensor technologies continue to evolve, the insights gained from this thesis could offer guidance for refining sensor models and hypothesis testing techniques, ultimately enhancing the efficiency and accuracy of sensor data analysis in various domains.