Transport Map Coupling Filter for State-Parameter Estimation
<|reference_start|>Transport Map Coupling Filter for State-Parameter Estimation: Many dynamical systems are subjected to stochastic influences, such as random excitations, noise, and unmodeled behavior. Tracking the system's state and parameters based on a physical model is a common task for which filtering algorithms, such as Kalman filters and their non-linear extensions, are typically used. However, many of these filters use assumptions on the transition probabilities or the covariance model, which can lead to inaccuracies in non-linear systems. We will show the application of a stochastic coupling filter that can approximate arbitrary transition densities under non-Gaussian noise. The filter is based on transport maps, which couple the approximation densities to a user-chosen reference density, allowing for straightforward sampling and evaluation of probabilities.<|reference_end|>
@article{grashorn2024transport,
    title={Transport Map Coupling Filter for State-Parameter Estimation},
    author={Jan Grashorn, Matteo Broggi, Ludovic Chamoin, Michael Beer},
    journal={Advances in Reliability, Safety and Security 2024, Part 9, pp.
  47-56, ISBN 978-83-68136-08-1},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02198},
    primaryClass={eess.SP cs.SY eess.SY}
}