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Low-rank tensor integration for Gaussian filtering of continuous time nonlinear systems
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main.pdf
Description
Accepted version
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345.66 KB
Format
Adobe PDF
Checksum (MD5)
a856bb8041289ea9f5c68e432b6c028d
Author(s)
Gorodetsky, Alex A.
Karaman, Sertac
Marzouk, Youssef M.
Date Issued
December 2017
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Gorodetsky, Alex A., Karaman, Sertac and Marzouk, Youssef M. 2017. "Low-rank tensor integration for Gaussian filtering of continuous time nonlinear systems."
Version
Author's final manuscript
Abstract
© 2017 IEEE. Integration-based Gaussian filters such as un-scented, cubature, and Gauss-Hermite filters are effective ways to assimilate data and models within nonlinear systems. Traditionally, these filters have only been applicable for systems with a handful of states due to stability and scalability issues. In this paper, we present a new integration method for scaling quadrature-based filters to higher dimensions. Our approach begins by decomposing the dynamics and observation models into separated, low-rank tensor formats. Once in low-rank tensor format, adaptive integration techniques may be used to efficiently propagate the mean and covariance of the distribution of the system state with computational complexity that is polynomial in dimension and rank. Simulation results are shown on nonlinear chaotic systems with 20 state variables.
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/CDC.2017.8264064
