Noise Robust Particle Event Velocimetry with A Kalman Filter-Based Tracking
Date of Award
5-5-2024
Degree Name
Ph.D. in Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Keigo Hirakawa
Abstract
Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream makes it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e.~causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based (FB) particle image/tracking velocimetry (PIV/PTV) as well as the state-of-the-art event-based (EB) particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.
Keywords
PEV, PIV, PTV, KF-PEV, Particle Velocimetry, Event-Based, Event Camera
Rights Statement
Copyright 2024, author
Recommended Citation
AlSattam, Osama A., "Noise Robust Particle Event Velocimetry with A Kalman Filter-Based Tracking" (2024). Graduate Theses and Dissertations. 7565.
https://ecommons.udayton.edu/graduate_theses/7565
