Motion Tomography via Occupation Kernels
The goal of motion tomography is to recover the description of a vector flow field using information about the trajectory of a sensing unit. In this paper, we develop a predictor corrector algorithm designed to recover vector flow fields from trajectory data with the use of occupation kernels developed by Rosenfeld et al. Specifically, we use the occupation kernels as an adaptive basis; that is, the trajectories defining our occupation kernels are iteratively updated to improve the estimation on the next stage. We show for a simulated example we have good accuracy in recovering the flow-field using a simple metric. We also apply our algorithm to real world data.
PDF AbstractCategories
Optimization and Control
Functional Analysis
93-08, 46E22
