Tensor-Train-based Algorithms for Aggregate State Estimation of Swarms with Interacting Agents
Name
root_edits.pdf
Description
Accepted version
Size
1.02 MB
Format
Adobe PDF
Checksum (MD5)
eb5214bf7ea4bc3dcccb4e7f5e167d5d
Author(s)
Miculescu, David
Karaman, Sertac
Date Issued
July 2020
Journal
Proceedings of the American Control Conference
Publisher
IEEE
Citation
Miculescu, David and Karaman, Sertac. 2020. "Tensor-Train-based Algorithms for Aggregate State Estimation of Swarms with Interacting Agents." Proceedings of the American Control Conference, 2020-July.
Version
Author's final manuscript
Abstract
© 2020 AACC. In this paper, we develop an efficient implementation of the gas-kinetic (GK) Probability Hypothesis Density (PHD) filter for aggregate swarm state estimation with interacting agents. We borrow a kinetic/mesoscopic partial differential equation (PDE) model of a swarm of interacting agents from biology moving in a plane with a heading state, which requires the computation of integrals up to five dimensions. In the context of the GK-PHD, we propagate this model by computing in a compressed format called the Tensor Train (TT) format, yielding better memory and runtime properties than a grid-based approach. Under certain assumptions, we prove that TT-GK-PHD has a time complexity of an order of magnitude better than the grid-based approach. Finally, we showcase the usefulness of our algorithm on a scenario which cannot be solved via the grid-based approach due to hardware memory constraints. Then in a computational experiment we demonstrate the better runtime and memory of TT-GK-PHD over the grid-based approach.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.23919/acc45564.2020.9147339
