Opportunity: Multi-Sensor Detection and Tracking of Low-Altitude Uncrewed Aircraft Systems

Research Problem: The rapid proliferation of small, low-altitude Uncrewed Aircraft Systems (UAS) presents unique security and airspace management challenges. Standard radar and surveillance systems often struggle to reliably detect, classify, and track these small, highly maneuverable platforms due to their low radar cross-section (RCS) and high ground clutter.

Project Objective: The objective of this project is to develop and evaluate sensor fusion and state estimation algorithms that combine heterogeneous sensor data to achieve robust, real-time tracking of non-cooperative UAS.

Participant Scope: The participant will model synthetic radar, optical, and acoustic sensor measurements of dynamic UAS flight paths. They will design and implement filtering algorithms to estimate aircraft states, handle missing data, and resolve measurement associations across multiple simulated sensor nodes.

Methods and Technologies: The student will utilize state estimation and tracking methodologies, such as Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), or Particle Filters. Development and simulation will be conducted using Python (NumPy, SciPy, FilterPy) or MATLAB, alongside open-source geospatial tools to model local terrain constraints.

Expected Deliverables: The project will yield a functional multi-sensor tracking simulation repository, a benchmark report evaluating tracking accuracy (e.g., Root Mean Square Error) under varying noise levels, and a final technical presentation detailing the performance of the implemented algorithms.