"Tracking of Uncooperative Targets Using Reachability Analysis" and "Parallelization of Rotor Noise Prediction"

Ryan Glenn

Speaker Bio: Ryan Glenn is a Ph.D. candidate studying aerospace engineering at Penn State. Ryan completed his B.S. in aerospace engineering from Penn State in 2022. His undergraduate research, led by Dr. Daning Huang, was on modeling hypersonic flow over compression ramps. While working with Dr. Puneet Singla, he has researched state estimation using sigma-point Kalman filters. He is currently investigating feedback solutions for pursuit-evasion games with uncertainty.

Abstract: This presentation presents a nonlinear filter to track a maneuvering target while accounting for a priori information about its maneuvering capabilities. The idea is to compute the probabilistic reachable set of the target given a priori distributions for its initial condition and maneuvering capabilities between two measurement time intervals. A minimum variance framework is used to assimilate sensor observation with model prediction to estimate the target state and corresponding state covariance. The performance of the derived filter is benchmarked against a conventional sigma-point filter by considering the problem of tracking an automobile on a road network.

Nitya Singh

Speaker Bio: Nitya Singh joined Penn State in 2021 and is pursuing research in computational rotor acoustics. They hold a dual degree (B.Tech. and M.Tech.) in Aerospace Engineering from IIT Bombay, awarded in 2019.

Abstract: Accurate and efficient rotor noise prediction is vital for the design and operation of electric Vertical Takeoff and Landing (eVTOL) aircraft in Advanced Air Mobility (AAM). Multi-rotor configurations introduce complex aerodynamic interactions and high computational demands when analyzing full flight trajectories and multiple observer locations. To address this, we are developing a parallel implementation of Farassat’s Formulation 1A of the Ffowcs Williams-Hawkings (FW-H) equation. Leveraging the equation’s linearity, the method parallelizes calculations across observers, time, and source surfaces using the Message Passing Interface (MPI). Results demonstrate significant reductions in runtime with no loss of accuracy, providing a scalable framework for noise prediction and flight path planning in modern eVTOL systems.