Infossm: Interpretable unsupervised learning of nonparametric state-space model for multi-modal dynamics

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 35
  • Download : 0
The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics model, Gaussian process (GP) have been widely used; GP can estimate the uncertainty of prediction and avoid over-fitting. Traditional GPSSMs, however, are based on Gaussian transition model, thus often have difficulty in describing a more complex transition model, e.g. aircraft motions. To resolve the challenge, this paper proposes a framework using multiple GP transition models which is capable of describing multi-modal dynamics. Furthermore, we extend the model to the information-theoretic framework, the so-called InfoSSM, by introducing a mutual information regularizer helping the model to learn interpretable and distinguishable multiple dynamics models. Two illustrative numerical experiments in simple Dubins vehicle and high-fidelity flight simulator are presented to demonstrate the performance and interpretability of the proposed model. Finally, this paper introduces a framework using InfoSSM with Bayesian filtering for air traffic control tracking.
Publisher
American Institute of Aeronautics and Astronautics Inc, AIAA
Issue Date
2019-01
Language
English
Citation

AIAA Scitech Forum, 2019

DOI
10.2514/6.2019-0681
URI
http://hdl.handle.net/10203/310942
Appears in Collection
AE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0