Probabilistic Undersea Search and Detection Modeling

This project explores how uncertainty about a moving undersea target's location evolves over time while an area is actively searched using one or more sonobuoys. The resulting simulation demonstrates the fundamentals of probabilistic tracking and introduces Bayesian updating, while providing a foundation for future extensions such as sonobuoy field-placement analysis and operational risk assessment.

Interns: Charlie Duva, David Liu

Mentor: Egzona Rexhepi (FPS/KVQ)