Article Contents
Article Contents

# Motion tomography via occupation kernels

• * Corresponding author: Benjamin P. Russo

§ This work was partially completed while the author was at Farmingdale State College SUNY.
Research supported by AFOSR Awards FA9550-20-1-0127 and FA9550-21-1-0134.
Research supported by NSF grants ECCS-2027976, ECCS-2027999 and NRI 2.0 - 1925147.
This work was partially completed while the author was at the University of Michigan.
** The following YouTube playlist discusses the contents of this manuscript: https://www.youtube.com/playlist?list=PLldiDnQu2phuBWvO40eiqNve6-_GOdSsz.
Notice: This manuscript has been authored, in part, by UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

• The goal of motion tomography is to recover a description of a vector flow field using measurements along 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. [9,10]. 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 in the next stage. Initial estimates are established, then under mild assumptions, such as relatively straight trajectories, convergence is proven using the Contraction Mapping Theorem. We then compare the developed method with the established method by Chang et al. [5] by defining a set of error metrics. We found that for simulated data, where a ground truth is available, our method offers a marked improvement over [5]. For a real-world example, where ground truth is not available, our results are similar results to the established method.

Mathematics Subject Classification: Primary: 93-08; Secondary: 46E22.

 Citation:

• Figure 1.  Algorithm 1, used for estimation of the vector field in (14). The true trajectories are calculated via RK4 over the time frame $[0, 1]$. Using Gaussian RBFs with a kernel width of 1, we performed $10$ iterations of Algorithm 1

Figure 2.  Calculated initial trajectories and output of Algorithm 1 for 5, 10, and 20 iterations on Gliderpalooza data

Figure 3.  A comparison of Algorithm 1 and the method of [5] for estimation of the vector field in (14)

Figure 4.  To demonstrate universality of the occupation kernel basis, Algorithm 1 is used to estimate three different flow fields. The flow field in (14) (Flow field 1), a linear flow field (Flow field 2), and a constant flow field (Flow field 3). The plot shows the average estimation error, as defined in 16, plotted against the number of iterates of Algorithm 1 for the three flow fields

Figure 5.  Simultaneous plots for estimation of the unknown flow field in the Gliderpalooza experiment using Algorithm 1 (green arrows) and the method of [5] (blue arrows)

Table 1.  Algorithm 1, along with the technique in [5] are used to estimate the vector field in (14). The table shows the maximum and the average estimation error, as defined in (15) and (16), respectively, for the two methods

 Method in [5] Algorithm 1 Max Error 1.1849 0.25321 Mean Error 0.51549 0.025642

Table 2.  Norm difference statistics between estimates of the Gliderpalooza flow field, approximated using Algorithm 1 and the method in [5]

 max norm difference 0.14877 mean norm difference 0.0088628 variance 0.0001869
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Figures(5)

Tables(2)